{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualization with Pandas (and Matplotlib)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# display plots in the notebook\n",
    "%matplotlib inline\n",
    "\n",
    "# increase default figure and font sizes for easier viewing\n",
    "plt.rcParams['figure.figsize'] = (8, 6)\n",
    "plt.rcParams['font.size'] = 14"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# read in the drinks data\n",
    "drink_cols = ['country', 'beer', 'spirit', 'wine', 'liters', 'continent']\n",
    "url = 'https://raw.githubusercontent.com/justmarkham/DAT8/master/data/drinks.csv'\n",
    "drinks = pd.read_csv(url, header=0, names=drink_cols, na_filter=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Histogram: show the distribution of a numerical variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
       "         0,   0,   1,   1,   1,   1,   2,   3,   5,   5,   5,   5,   5,\n",
       "         6,   6,   6,   6,   8,   8,   8,   9,   9,   9,   9,  12,  13,\n",
       "        15,  15,  16,  16,  17,  18,  19,  19,  20,  20,  21,  21,  21,\n",
       "        21,  22,  23,  25,  25,  25,  25,  26,  28,  31,  31,  31,  31,\n",
       "        32,  32,  34,  36,  36,  36,  37,  42,  42,  43,  44,  45,  47,\n",
       "        49,  51,  51,  52,  52,  52,  53,  56,  56,  57,  58,  60,  62,\n",
       "        62,  63,  64,  69,  71,  76,  76,  77,  77,  77,  78,  79,  82,\n",
       "        82,  85,  88,  89,  90,  92,  93,  93,  98,  99, 102, 105, 106,\n",
       "       109, 111, 115, 120, 122, 124, 127, 128, 130, 133, 140, 142, 143,\n",
       "       144, 147, 149, 149, 152, 157, 159, 162, 163, 167, 169, 171, 173,\n",
       "       185, 188, 192, 193, 193, 194, 194, 196, 197, 199, 203, 206, 213,\n",
       "       217, 219, 224, 224, 225, 230, 231, 233, 234, 236, 238, 240, 245,\n",
       "       245, 247, 249, 251, 261, 263, 263, 270, 279, 281, 283, 284, 285,\n",
       "       295, 297, 306, 313, 333, 343, 343, 346, 347, 361, 376], dtype=int64)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# sort the beer column and mentally split it into 3 groups\n",
    "drinks.beer.order().values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xbfb55c0>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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cA/gi8Eng74C3T6QySZI0NaNeKOk+4L8meQvwTLpgcUVVXTPJ4iRJ0nSsGhCS/Bvg1+ku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\nBgRJktQwIEiSpIYBQZIkNf4//cC9HhIYVgwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x41b4198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# compare with histogram\n",
    "drinks.beer.plot(kind='hist', bins=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xc1c7e10>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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onZ8AfpoFbHN/DPJQHv0PeK7bl+R8Hv1w/NrQ4oXvvz20b6X1F6r/VrAf8IQR\n/y9ZtLZBv30rLViwvrsUOAJ4YX86Evgy8PH+z19nkv0367MfH8fZkq+mO+PxN+g+MM+nOyPyGbOu\nbS/b8V66pHcY8BLgc8D3d7aD7rKX7wO/1P8F+QRdql0969p30Z7V/V/YI+ke6PSO/s8jtwf4E7pL\nfl5Bd8nP1cBG+jesmtf29Ze9FzgWeCawnm688luL0D7gQmA7cCKPvZRqsPaF7b89tW8J+u8P6T4Q\nnkl3KfF5dEdKf27R+25P7Vv0vttFe3vABQOvJ9Z/M2/s4/wLejPdt5IHgS/RjZ/MvK69bMPHgTvp\nwswWuocd/fTQOucA36Y76eVquicizrz2XbRnPbCjPz0y8PN/H7U9wI8Af0R32Os+4LPAobNu257a\nR3dG8uV0J938E3B7f/6hQ9uYy/at0Kad0+/vze/jorZvCfrvw/2aH+y34Qr6H/6L3nd7at+i990u\n2vvPlwFOuv+8FbAkSQ1aqHMAJEnSeBgAJElqkAFAkqQGGQAkSWqQAUCSpAYZACRJapABQJKkBhkA\nJElqkAFAkqQG/X+w1WeM/jTsPwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x41b4080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# try more bins\n",
    "drinks.beer.plot(kind='hist', bins=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0xc3936d8>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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RAJIkTZm1JgBV9cIxxSFJksbIPgCSJHWQCYAkSR1kAiBJUgeZAEiS1EEmAJIk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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xc1be828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# add title and labels\n",
    "drinks.beer.plot(kind='hist', bins=20, title='Histogram of Beer Servings')\n",
    "plt.xlabel('Beer Servings')\n",
    "plt.ylabel('Frequency')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xc6bf898>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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DiSeGnw8/DHXqxB2RiEh+03RTKWolJfDAA7BiRXhh2apVcUckIiIVlFhIXqpb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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xc6c9da0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# compare with density plot (smooth version of a histogram)\n",
    "drinks.beer.plot(kind='density', xlim=(0, 500))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Scatter Plot: show the relationship between two numerical variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  0,   0],\n",
       "       [  0,  74],\n",
       "       [  0,   0],\n",
       "       [  0,   0],\n",
       "       [  0,   0],\n",
       "       [  0,   0],\n",
       "       [  0,   0],\n",
       "       [  0,   0],\n",
       "       [  0,   0],\n",
       "       [  0,   0],\n",
       "       [  0,   0],\n",
       "       [  0,   0],\n",
       "       [  0,   0],\n",
       "       [  0,   0],\n",
       "       [  0,   0],\n",
       "       [  1,   7],\n",
       "       [  1,   1],\n",
       "       [  1,   4],\n",
       "       [  1,   1],\n",
       "       [  2,   0],\n",
       "       [  3,   1],\n",
       "       [  5,   0],\n",
       "       [  5,   0],\n",
       "       [  5,  16],\n",
       "       [  5,   1],\n",
       "       [  5,   0],\n",
       "       [  6,   1],\n",
       "       [  6,   0],\n",
       "       [  6,   1],\n",
       "       [  6,   9],\n",
       "       [  8,   0],\n",
       "       [  8,   1],\n",
       "       [  8,   1],\n",
       "       [  9,   2],\n",
       "       [  9,   0],\n",
       "       [  9,   7],\n",
       "       [  9,   0],\n",
       "       [ 12,  10],\n",
       "       [ 13,   0],\n",
       "       [ 15,   3],\n",
       "       [ 15,   1],\n",
       "       [ 16,   5],\n",
       "       [ 16,   0],\n",
       "       [ 17,   1],\n",
       "       [ 18,   0],\n",
       "       [ 19,  32],\n",
       "       [ 19,   2],\n",
       "       [ 20,   0],\n",
       "       [ 20,  31],\n",
       "       [ 21,  11],\n",
       "       [ 21,  11],\n",
       "       [ 21,   5],\n",
       "       [ 21,   1],\n",
       "       [ 22,   1],\n",
       "       [ 23,   0],\n",
       "       [ 25,   8],\n",
       "       [ 25,  14],\n",
       "       [ 25,   2],\n",
       "       [ 25,   7],\n",
       "       [ 26,   4],\n",
       "       [ 28,  21],\n",
       "       [ 31, 128],\n",
       "       [ 31,   6],\n",
       "       [ 31,  10],\n",
       "       [ 31,   1],\n",
       "       [ 32,   4],\n",
       "       [ 32,   1],\n",
       "       [ 34,  13],\n",
       "       [ 36,  19],\n",
       "       [ 36,   5],\n",
       "       [ 36,   1],\n",
       "       [ 37,   7],\n",
       "       [ 42,   2],\n",
       "       [ 42,   7],\n",
       "       [ 43,   0],\n",
       "       [ 44,   1],\n",
       "       [ 45,   0],\n",
       "       [ 47,   5],\n",
       "       [ 49,   8],\n",
       "       [ 51,  20],\n",
       "       [ 51,   7],\n",
       "       [ 52,   2],\n",
       "       [ 52, 149],\n",
       "       [ 52,  26],\n",
       "       [ 53,   2],\n",
       "       [ 56, 140],\n",
       "       [ 56,   1],\n",
       "       [ 57,   1],\n",
       "       [ 58,   2],\n",
       "       [ 60,  11],\n",
       "       [ 62,  18],\n",
       "       [ 62, 123],\n",
       "       [ 63,   9],\n",
       "       [ 64,   4],\n",
       "       [ 69,   2],\n",
       "       [ 71,   1],\n",
       "       [ 76,   8],\n",
       "       [ 76,   9],\n",
       "       [ 77,   8],\n",
       "       [ 77,  16],\n",
       "       [ 77,   1],\n",
       "       [ 78,   1],\n",
       "       [ 79,   8],\n",
       "       [ 82,   9],\n",
       "       [ 82,   0],\n",
       "       [ 85, 237],\n",
       "       [ 88,   0],\n",
       "       [ 89,  54],\n",
       "       [ 90,   2],\n",
       "       [ 92, 233],\n",
       "       [ 93,   5],\n",
       "       [ 93,   1],\n",
       "       [ 98,  18],\n",
       "       [ 99,   1],\n",
       "       [102,  45],\n",
       "       [105,  24],\n",
       "       [106,  86],\n",
       "       [109,  18],\n",
       "       [111,   1],\n",
       "       [115, 220],\n",
       "       [120,  11],\n",
       "       [122,  51],\n",
       "       [124,  12],\n",
       "       [127, 370],\n",
       "       [128,   7],\n",
       "       [130, 172],\n",
       "       [133, 218],\n",
       "       [140,   9],\n",
       "       [142,  42],\n",
       "       [143,  36],\n",
       "       [144,  16],\n",
       "       [147,   4],\n",
       "       [149, 120],\n",
       "       [149,  11],\n",
       "       [152, 186],\n",
       "       [157,  51],\n",
       "       [159,   3],\n",
       "       [162,   3],\n",
       "       [163,  21],\n",
       "       [167,   8],\n",
       "       [169, 129],\n",
       "       [171,  71],\n",
       "       [173,  35],\n",
       "       [185, 280],\n",
       "       [188,   7],\n",
       "       [192, 113],\n",
       "       [193,   9],\n",
       "       [193, 221],\n",
       "       [194, 339],\n",
       "       [194,  32],\n",
       "       [196, 116],\n",
       "       [197,   7],\n",
       "       [199,  28],\n",
       "       [203, 175],\n",
       "       [206,  45],\n",
       "       [213,  74],\n",
       "       [217,  45],\n",
       "       [219, 195],\n",
       "       [224,  59],\n",
       "       [224, 278],\n",
       "       [225,  81],\n",
       "       [230, 254],\n",
       "       [231,  94],\n",
       "       [233,  78],\n",
       "       [234, 185],\n",
       "       [236, 271],\n",
       "       [238,   5],\n",
       "       [240, 100],\n",
       "       [245, 312],\n",
       "       [245,  16],\n",
       "       [247,  73],\n",
       "       [249,  84],\n",
       "       [251, 190],\n",
       "       [261, 212],\n",
       "       [263,  97],\n",
       "       [263,   8],\n",
       "       [270, 276],\n",
       "       [279, 191],\n",
       "       [281,  62],\n",
       "       [283, 127],\n",
       "       [284, 112],\n",
       "       [285,  18],\n",
       "       [295, 212],\n",
       "       [297, 167],\n",
       "       [306,  23],\n",
       "       [313, 165],\n",
       "       [333,   3],\n",
       "       [343,  56],\n",
       "       [343,  56],\n",
       "       [346, 175],\n",
       "       [347,  59],\n",
       "       [361, 134],\n",
       "       [376,   1]], dtype=int64)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# select the beer and wine columns and sort by beer\n",
    "drinks[['beer', 'wine']].sort('beer').values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xdbbe390>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Vg8GC1Cae+BZmepKsiYkzmZg4k7Vr1/u+SR1isCC1QTNPfP0SdFx++dXlbJrrgWJmzemR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\nUkMGC5IkqaH/C1ea/TyvoNsAAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xdbc5ef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# compare with scatter plot\n",
    "drinks.plot(kind='scatter', x='beer', y='wine')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xdcbabe0>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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6IaVUldp01e3t7fzVXz1MRcXbAXjttZ9w9dW1w8GCjC60wxBmtsbMTqY8Okco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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xdfafbe0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# add transparency\n",
    "drinks.plot(kind='scatter', x='beer', y='wine', alpha=0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xdfc2048>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ir+vsALoDCwIWcC4s4RtjipSHH3qI7t0v5ejhQ5wVFcWM/7zKRx/NDcq9Hnnk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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xdfe34a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# vary point color by spirit servings\n",
    "drinks.plot(kind='scatter', x='beer', y='wine', c='spirit', colormap='Blues')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x0000000018B46AC8>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000000018D82240>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000000019098710>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x00000000190B8320>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x00000000191DDA90>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x00000000191EBA58>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x000000001932E0F0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x00000000193565C0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000000019479400>]], dtype=object)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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OHWcs7pFveDI1DN/aumT4/SxrGpA7iUm2gOxkGFSHSr3T2bkDOB97mYxexo7d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\nI7dYLBaLJYuxjtxisVgslizm/wFsy0ifXH7z6gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xdfe99e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# scatter matrix of three numerical columns\n",
    "pd.scatter_matrix(drinks[['beer', 'spirit', 'wine']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x00000000196C0AC8>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000000019D2B320>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000000019D53160>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x0000000019EAE8D0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000000019FD4940>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000000019FE76D8>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x000000001A11E2B0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000000001A144780>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000000001A2A52B0>]], dtype=object)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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s3F2S51ZEeAs4UlgRIV7fY6XWb+WZeAo8CHPOjRa3zeyfyAde7ynsmgUcq/R3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\nERERCYGCMBEREZEQ/H/MB4ksIkGkjgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x196ac7b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# increase figure size\n",
    "pd.scatter_matrix(drinks[['beer', 'spirit', 'wine']], figsize=(10, 8))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bar Plot: show a numerical comparison across different categories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AF    53\n",
       "EU    45\n",
       "AS    44\n",
       "NA    23\n",
       "OC    16\n",
       "SA    12\n",
       "dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# count the number of countries in each continent\n",
    "drinks.continent.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a5ef3c8>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x196b1dd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# compare with bar plot\n",
    "drinks.continent.value_counts().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>beer</th>\n",
       "      <th>spirit</th>\n",
       "      <th>wine</th>\n",
       "      <th>liters</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>continent</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AF</th>\n",
       "      <td>61.471698</td>\n",
       "      <td>16.339623</td>\n",
       "      <td>16.264151</td>\n",
       "      <td>3.007547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AS</th>\n",
       "      <td>37.045455</td>\n",
       "      <td>60.840909</td>\n",
       "      <td>9.068182</td>\n",
       "      <td>2.170455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EU</th>\n",
       "      <td>193.777778</td>\n",
       "      <td>132.555556</td>\n",
       "      <td>142.222222</td>\n",
       "      <td>8.617778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NA</th>\n",
       "      <td>145.434783</td>\n",
       "      <td>165.739130</td>\n",
       "      <td>24.521739</td>\n",
       "      <td>5.995652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OC</th>\n",
       "      <td>89.687500</td>\n",
       "      <td>58.437500</td>\n",
       "      <td>35.625000</td>\n",
       "      <td>3.381250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SA</th>\n",
       "      <td>175.083333</td>\n",
       "      <td>114.750000</td>\n",
       "      <td>62.416667</td>\n",
       "      <td>6.308333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 beer      spirit        wine    liters\n",
       "continent                                              \n",
       "AF          61.471698   16.339623   16.264151  3.007547\n",
       "AS          37.045455   60.840909    9.068182  2.170455\n",
       "EU         193.777778  132.555556  142.222222  8.617778\n",
       "NA         145.434783  165.739130   24.521739  5.995652\n",
       "OC          89.687500   58.437500   35.625000  3.381250\n",
       "SA         175.083333  114.750000   62.416667  6.308333"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# calculate the mean alcohol amounts for each continent\n",
    "drinks.groupby('continent').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a5ef358>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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pSZIyZUhLkpSprpWugCovIipdBUlSEwxpFaUW1hvkktTeDGlJqhKt6RVLqaUT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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a5e6a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# side-by-side bar plots\n",
    "drinks.groupby('continent').mean().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a8a4080>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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/66bM/GBjq2q8iHgaeGdmPrya9W8FbsvMMX1bWbmq31EfpLJh/BbgR8C3gB9m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\nT90KlQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ad81eb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# drop the liters column\n",
    "drinks.groupby('continent').mean().drop('liters', axis=1).plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1ae85d30>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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GtCRJhTKkJUkqlCEtSVKhDGlJkgplSEuSVChDWpKkQhnSkiQVKjLLut5yRGRv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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ae52d30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# stacked bar plots\n",
    "drinks.groupby('continent').mean().drop('liters', axis=1).plot(kind='bar', stacked=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Box Plot: show quartiles (and outliers) for one or more numerical variables\n",
    "\n",
    "**Five-number summary:**\n",
    "\n",
    "- min = minimum value\n",
    "- 25% = first quartile (Q1) = median of the lower half of the data\n",
    "- 50% = second quartile (Q2) = median of the data\n",
    "- 75% = third quartile (Q3) = median of the upper half of the data\n",
    "- max = maximum value\n",
    "\n",
    "(More useful than mean and standard deviation for describing skewed distributions)\n",
    "\n",
    "**Interquartile Range (IQR)** = Q3 - Q1\n",
    "\n",
    "**Outliers:**\n",
    "\n",
    "- below Q1 - 1.5 * IQR\n",
    "- above Q3 + 1.5 * IQR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
       "         0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   1,   1,   1,\n",
       "         1,   1,   1,   1,   1,   1,   2,   2,   2,   2,   2,   2,   2,\n",
       "         3,   3,   3,   3,   3,   3,   3,   3,   4,   4,   4,   5,   5,\n",
       "         6,   6,   6,   7,   9,  11,  11,  12,  13,  15,  15,  16,  16,\n",
       "        18,  18,  18,  18,  19,  21,  21,  22,  22,  25,  25,  27,  29,\n",
       "        31,  31,  34,  35,  35,  35,  35,  38,  39,  41,  41,  42,  42,\n",
       "        44,  46,  50,  51,  55,  56,  57,  60,  61,  63,  63,  65,  67,\n",
       "        68,  69,  69,  69,  71,  71,  72,  74,  75,  76,  76,  79,  81,\n",
       "        84,  87,  87,  88,  97,  97,  98,  98, 100, 100, 100, 100, 101,\n",
       "       104, 104, 112, 114, 114, 114, 117, 117, 118, 118, 122, 122, 124,\n",
       "       126, 128, 131, 132, 133, 133, 135, 137, 138, 145, 147, 151, 152,\n",
       "       154, 156, 157, 158, 160, 170, 173, 173, 176, 178, 179, 186, 189,\n",
       "       192, 194, 200, 202, 205, 215, 215, 216, 221, 226, 237, 244, 246,\n",
       "       252, 254, 258, 286, 293, 302, 315, 326, 326, 373, 438], dtype=int64)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# sort the spirit column\n",
    "drinks.spirit.order().values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    193.000000\n",
       "mean      80.994819\n",
       "std       88.284312\n",
       "min        0.000000\n",
       "25%        4.000000\n",
       "50%       56.000000\n",
       "75%      128.000000\n",
       "max      438.000000\n",
       "Name: spirit, dtype: float64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# show \"five-number summary\" for spirit\n",
    "drinks.spirit.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1b301a20>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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c3S0JcMUxaZyc3S1J0hxjSEuS1ChDWpKkRhnSkiQ1ypCWBLh2t9QiZ3dLkjRmzu6WJGmO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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1b593a58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# compare with box plot\n",
    "drinks.spirit.plot(kind='box')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1b6d47f0>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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P+nh++ljn9aSP56ePdd4fnyctSVLPGKQlSeoog7QkSR1lkJYkqaMM0pIkdZRB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\nHWWQliSpowzSkiR1lEFakqSO+v9E1Xhz/jcyHQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ae5c4e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# include multiple variables\n",
    "drinks.drop('liters', axis=1).plot(kind='box')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Line Plot: show the trend of a numerical variable over time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# read in the ufo data\n",
    "url = 'https://raw.githubusercontent.com/justmarkham/DAT8/master/data/ufo.csv'\n",
    "ufo = pd.read_csv(url)\n",
    "ufo['Time'] = pd.to_datetime(ufo.Time)\n",
    "ufo['Year'] = ufo.Time.dt.year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1930       2\n",
       "1931       2\n",
       "1933       1\n",
       "1934       1\n",
       "1935       1\n",
       "1936       2\n",
       "1937       2\n",
       "1939       3\n",
       "1941       2\n",
       "1942       3\n",
       "1943       5\n",
       "1944       8\n",
       "1945       9\n",
       "1946       8\n",
       "1947      41\n",
       "1948       9\n",
       "1949      19\n",
       "1950      31\n",
       "1951      21\n",
       "1952      52\n",
       "1953      36\n",
       "1954      55\n",
       "1955      33\n",
       "1956      46\n",
       "1957      78\n",
       "1958      53\n",
       "1959      57\n",
       "1960      67\n",
       "1961      50\n",
       "1962      72\n",
       "        ... \n",
       "1985     211\n",
       "1986     186\n",
       "1987     210\n",
       "1988     232\n",
       "1989     247\n",
       "1990     237\n",
       "1991     220\n",
       "1992     245\n",
       "1993     292\n",
       "1994     406\n",
       "1995    1344\n",
       "1996     851\n",
       "1997    1237\n",
       "1998    1743\n",
       "1999    2774\n",
       "2000    2635\n",
       "2001    2925\n",
       "2002    2933\n",
       "2003    3507\n",
       "2004    3850\n",
       "2005    3787\n",
       "2006    3445\n",
       "2007    4058\n",
       "2008    4655\n",
       "2009    4251\n",
       "2010    4154\n",
       "2011    5089\n",
       "2012    7263\n",
       "2013    7003\n",
       "2014    5382\n",
       "dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# count the number of ufo reports each year (and sort by year)\n",
    "ufo.Year.value_counts().sort_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1b6e74a8>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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fDd26FaRazYZ65CIiklMrVkDv3tX3mcGjj4aFcNI46pGLiEhOrVxZfWg9TivV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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1b6ef668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# compare with line plot\n",
    "ufo.Year.value_counts().sort_index().plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c2d0b38>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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L6g1DWtKkbMmSesMWLEmTmjsXPvhB+MAHej0SSa0ypKUhcsop8OMfw1VX9XokklphSEtD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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c2c8a20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# don't use a line plot when there is no logical ordering\n",
    "drinks.continent.value_counts().plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Grouped Box Plots: show one box plot for each group"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1969d898>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c2980b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# reminder: box plot of beer servings\n",
    "drinks.beer.plot(kind='box')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c2dfcf8>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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azZ1zlq7XG206hNZZvny06RBap43HmZvoE/giO5uUJm0dy0Omn6U6daUrok7O\nWbpcc+Ym+pqMj/eaDqFVunJPt4jik1/D1FuxorZtiQy/jRZEr+kAWqjXdAAt1Gs6gGS+in4G1avo\nly+HRYuKf/sqejMzy5mb6BO4id4mk2/TXZ5x1a0r+1kn5yxdrjlzE72ZmW3gsei7wRV8gpGRXtMh\ntEpX+uDr5Jyla+MY4U3zWPTp2nicuYJPsHhx0xGYWb82jhFu7dPG48x98GbzlG/fXJ5xmVl93Adv\nZmbWMa7gE7h/NI3zlc45S+ecpXPO0rUxZ67gzcw6xmPRd8OMFbykd0u6VNLtklZL+qqkJ09Sbomk\nGyTdLWmFpL361m8l6VRJt0i6U9JXJO1W584sNI8Tnsb5SuecpWvjGOFN81j06dp4nM14kZ2k84Gz\ngUspfhB8AHgOsFdErCnLvBN4L3AUcDXwfmB/4EkRcWdZ5jTgpcCRwG3AR4ERYN+IWF95P19kZ62S\n68VsucZVt67sZ52cs3S55mxeF9lFxCERsTwiroiIXwBHADsB+5UbF3A8cHJEnBcRl1NU9A8HDi/L\nbAccA5wQERdFxE/K7TwVOGjeezggbeyDaZLzlc45m4te0wG0UK/pAFqo13QAyebSB/+I8nVryvnH\nAjsD35koEBH3At+j/BEA7Ats2VdmFXBlpYyZmZnVJPk+eEnnAI8HnhERIWk/4PvAo8tKe6LcmcCu\nEXGIpMOB5RGxZd+2LgKujog3Vpa5id5aJd+muzzjqltX9rNOzlm6XHM2XRN90tPkJH2U4ox7/1nW\nwnNKx9jYGIvKx7aNjIywePHiDRcfTTRhet7zOc1DXvF0bd75T5s/6aS84vH87OdXrlzJ2rVrARgf\nH2c6sz6Dl/Qx4BXAARFxdWX544BfA8+MiMsqy78BrI6IoyUdCFwI7BQRt1bKXA6cExFLK8uyPYPv\n9XobEm0z60q+6vxlX2fOcj3jANhhB1izZuZys9NjooKfr+23h9tuq2VTWevCZ7PeYwxyPc7mPZKd\npI8DrwQOrFbupeuAm4CDK+W3priK/pJy0WXA/X1ldgf2qJQxs45Ys6b48VHHtGJFfduqt0KwJtV5\njLX1OJvNbXKfAF4N/BXFRXET1kXEXWWZdwDvAY4GfgWcyMbb5CbKfBJ4CTDGxtvktqO4TW5DEDmf\nwZtNJtcz5VzjgnxjyzUuS5fz37LO2KY7g59NBb+eoi+9fwNLIuIDlXInAa8Htgd+BLwpIq6orH8I\n8BGKW+fFcF+oAAAc5UlEQVS2oWiyPzYibuh7P1fw1iq5fpHkGhfkG1uucVm6nP+Wg6rgZ3Mf/IMi\nYovy/9XpA33llkbErhGxTUQcUK3cy/X3RcRxEbFjRGwbES/rr9xzt/GCHpsN5yudc5bOOUvnnKVr\nY87mch+8mZm1mMei7wY/D95snnJtCsw1Lsg3tlzjqlsX9jPnfcymid7MzMzaxxV8gjb2wTTJ+Urn\nnKVzzuai13QArdPG48wVvJmZ2RByH7zZPOXa15drXJBvbLnGVbcu7GfO++g+eDMzWxAnndR0BDYI\nruATtLEPpknOVzrnLJ1zlm50tNd0CK3TxuPMFbyZmdkQch+82Tzl2teXa1yQb2y5xmXpcv5bug/e\nzMzM5swVfII29sE0yflK55ylc87SOWfp2pgzV/BmZh3jsei7wX3wCXo9GB1tOgrLTa59fbnGBfnG\nlmtcdevCfua8j+6Dz1ALW2jMzKyjXMEnGB/vNR1Cq7Sxz6ppzlk652wuek0H0DptPM4e3HQAuev1\nNp65L18OixYV/x4ddXO9mZnly33wCZYsKSazqlz7+nKNC/KNLde46taF/cx5H90Hb2ZmC8Jj0XeD\nK/gEIyO9pkNolTb2WTXNOUvnnKXzWPTp2nicuYJPsHhx0xGYmZnNjvvgzeYp176+XOOCfGPLNS5L\nl/Pf0n3wZmZmNmeu4BO0sQ+mSc5XOucsnXOWzjlL18ac+T54M7OOWbZs+MfxCASTNlw3Lyr/XUju\ngzebp1z7+nKNC/KNLde46taF/cx5H90Hb2ZmZnPmCj5BG/tgmuR8pXPO0jlnc9FrOoDWaeNx5gre\nzMxsCM3YBy/pecAJwNOBXYGjI2J5Zf0y4Mi+l/0oIvarlNkK+AhwGLANcBFwbETcMMn7DbwPXqr3\nSgxfQ9Atufb15RoX5BtbrnHVrQv7mfM+5tQHvy3wM+AtwD1sfulfABcAu1Smv+wrcwpwKEUF/1zg\nEcDXJWXRghARs5pgtuXMzPLlsei7YcYKNiK+FREnRsS5wPpJigi4LyJWV6a1G1ZK2wHHACdExEUR\n8RPgCOCpwEH17Mag9JoOoFXa2GfVNOcsnXOWzmPRp2vjcVbHGXQA+0u6WdJVkk6XtFNl/b7AlsB3\nNrwgYhVwJbAfZmZmVruk++AlrQPeFBH/Xln2SuAu4DrgscA/AlsA+0bEfZIOB5ZHxJZ927oIuDoi\n3ti3PNv74HPu07Hm5Hpc5BoX5BtbrnFZupz/loPqg5/3SHYR8cXK7OWSLgN+A7wIOG8u2xwbG2PR\nokUAjIyMsHjxYkbLYZcmmkmamD/ppGbf3/P5zkNe8eQ+n2u+oEevl088nh/O+fkc/ytXrmTt2qIX\nfHx8nOnM+wx+inLXAqdFxIclHQhcCOwUEbdWylwOnBMRS/tem+0ZfK/Xq3wR2Ey6kq86f43XmbOu\nnMF0JWd16sJns+6/Za7H2UBHsiv733cDbiwXXQbcDxxcKbM7sAdwSd3vb2Zm01u2rOkIbBBmcx/8\ntsATy9kfAB8EvgbcCtwGLAW+DNwELAJOpqjg94yIu8ptfBJ4CTBWvuajwHYU/fSbBJDzGbzZZHI9\n68s1Lsg3tlzjqlsX9jPnfRzUGfxsKvhR4OJyNtj4fJ5lwLHAfwBPA0YoztovBt5XHcRG0kMoBro5\nnGKgmwvJaKAbs/nI9Ysk17gg39hyjatuXdjPnPcxmyb6iOhFxIPKaYvKv4+JiHsj4pCI2DkitoqI\nReXyG/q2cV9EHBcRO0bEthHxsskq99xtvEDCZsP5SuecpXPO5qLXdACt08bjzM+DT9CFZyibDUKu\nz+oe1HO6zQbBz4NPkHOTjzUn1+Mi17gg39hyjatuXdjPnPexNffBWzf5AT1m7eWx6Luh9tvkhluv\n6QCyMbuH7qxIeJCPQTv7+ZrmnKXzWPTp2nicuYI3MzMbQu6DT5Bzn06OupKvXPcz17gg39hyjcvS\n5fy3zOY2OdvI/VZmZtYWruATuN8qzVFH9ZoOoXXa2M/XNOcsnXOWro05cwVvC2ZsrOkIzGwyHou+\nG9wHbzZPufb15RoX5BtbrnHVrQv7mfM+ug/ezMzM5swVfII29sE0yflK55ylc87motd0AAMh1Tn1\natvW9tsPZv9dwSdwv5WZWTtE1DvVuc3bbhtMDtwHnyDnPp0cLVlSTMOu5lF7a7P99oP7IkmV62cp\n17jq1pX9rFOuOZvX8+AHzRX88HC+0nUlZ7nuZ65xAeywA6xZ03QUm8v5h2Sdcj02fJFdbXpNB9Ay\nvaYDaKFe0wG0Tlf64Nesqa+JeMWKXm3byvFHx8LoNR1AMlfwZmZmQ8hN9AlybaLJlfOVris5y3U/\nc40L8o0t17jqlus1RW6ir4nHojcz66YcK/eZuIJP4LHo03gs+nTOWbqu9MHXyTlL18acuYK3BeOx\n6NM5Z2ZWF/fBm9nA5dpvm2tckG9sucbVFe6DNzMz6xhX8Ana2AfTJOcrnXOWzjlL55ylGxvrNR1C\nMlfwCTwWvZlZNy1f3nQE6dwHn8B9TWlyvW80Z13JWa6fpVzjgnxjyzWuuuW6nx6Lvia5/oFz5Xyl\n60rOct3PXOOCfGPLNa665bqfvsiuNr2mA2iZXtMBtFCv6QBax/3J6Zyzueg1HUAyV/BmZmZDyE30\nCXJtosmV85WuKznLdT9zjQvyjS3XuOqW6/Ux82qil/Q8SV+VtErSeklHTVJmiaQbJN0taYWkvfrW\nbyXpVEm3SLpT0lck7Tb3XWqGx6I3M+umHCv3mcymiX5b4GfAW4B7gE1+q0l6J/BW4M3AM4HVwAWS\nHlYpdgpwKHAY8FzgEcDXJbWqi8Bj0afxuOrpnLN07k9O55yla2POZqxgI+JbEXFiRJwLrK+ukyTg\neODkiDgvIi4HjgIeDhxeltkOOAY4ISIuioifAEcATwUOqnVvLCseVz2dc2ZmdUnqg5e0DnhTRPx7\nOf844NfAMyPiskq5rwO/j4gxSQcCFwI7RcStlTK/AL4cEUv63iPbPngzq0eu/ba5xgX5xpZrXF2x\nkLfJ7VL+/+a+5asr63YBHqhW7pXX7DzP9zczM7NJPHgBtz3n33RjY2MsWrQIgJGRERYvXszo6Ciw\nsR+kiflqH0wO8eQ+73ylz59yyinZHO8LOQ/1bW/lypUcf/zxtWwPevR6zedn4fO/6Wc0p79nrvNj\nY70NXWhNxrNy5UrWrl0LwPj4ONNZqCb6bwCrI+LoaZroLwfOiYilfe+RbRP92FiPZctGmw6jNXq9\nXuWL02ajKzmrs1m3zpzl3NzsnDVL6hEx2nQYm1nIJvrrgJuAgytvtjWwP3BJuegy4P6+MrsDe1TK\ntMLy5aNNh9Aqvd5o0yG0jnOWrgs/iOrmnM3FaNMBJJvxDF7StsATy9kfAB8EvgbcGhHXS3oH8B7g\naOBXwIkUFfyTIuKuchufBF4CjAG3AR8FtgP27T9dz/kMviu/VOvifKXrSs5y3c9c44J8Y8s1rrrl\nup/zPYN/JvDjctoaWFr+eylARHwI+BjwCeBSigvnDp6o3EvHA+cBXwS+D9wBvCTbmnxKvaYDaJle\n0wG0UK/pAFqn2q9ss+OczUWv6QCSzXiRXUT0mOGHQNmPvnSa9fcBx5WTmZlZForhXGZbduYyOZ23\neiz6BLk20eTK+UrXlZzlup+5xgX5xpZrXF3hx8XWxGPRm5lZW7iCT+Cx6NN4XPV0zlk69yenc87S\ntTFnruBtwXhc9XTOmZnVxX3wZjZwufbb5hoX5BtbrnF1hfvgzczMOsYVfII29sE0yflK55ylc87S\nOWfp2pgzV/AJli1rOgIzM7PZcR98Avc1pVmypJhs9rqSs1w/S7nGBfnGlmtcXTFdH7wr+AQ+kNM4\nX+m6krNc9zPXuCDf2HKNqyt8kV1tek0H0DK9pgNooV7TAQyMVNfUq21b22/fdFYGo439yU1rY85m\nHIvezKxudZ7x+QzSbHJuok/gL5I0zlc65yxdV3KW637mGldXTNdEP9Rn8DvsAGvW1LvNhAcPTWv7\n7eG22+rZlpmZWb+h7oNfs6b4ZVnXtGJFr7Zt1f3DI0ceVz2dczYXvaYDaJ029ic3rY05G+oK3prl\ncdXTOWdmVpeh7oPPuW8o59jM2sRjBzQr17i6orP3wed84OUcm5nlJ9fvjFzj6grfB1+TNvbBNMn5\nSuecpXPO0jln6dqYM1fwZmZmQ8hN9A3JOba6dKVvtE7OmU0l1++MXOPqCvfBZyjn2OrShX2sm3Nm\nU8n12Mg1rq5wH3xN2tgH06xe0wG0UK/pAFpnbKzXdAit4++ydG3MmSt4M2u15cubjsAsT26ib0jO\nsdWlC/tYN+csXVdylut+5hpXV7iJ3szMrGNcwSdoYx9Mqh12qO853VDfc7qlIrZh57Ho56LXdACt\n04Xvsrq1MWeu4G0TdT6gZ8WKeh/204UH9HgsejOri/vgG5JrbLnGBXnHZs3pytgBuR7/ucbVFQve\nBy9piaT1fdPvJilzg6S7Ja2QtFcd721m3daFyt1sLupsov8lsEtl2ntihaR3Am8F3gw8E1gNXCDp\nYTW+/4JrYx9Mk5yvdM5ZOucsnXOWro05e3CN23ogIlb3L5Qk4Hjg5Ig4r1x2FEUlfzhweo0xmJmZ\nGTX1wUtaArwdWAv8AfhP4D0RcZ2kxwG/Bp4ZEZdVXvN14PcRMda3LffBNyjXuCDv2OrSlf5kmwNN\n2s2ah2H/YGZswceil3QI8DCKZvqdgROBPYAnl///PvDoiFhVec2ZwK4RcUjftlzBNyjXuCDv2OrS\nhX20ucn12Mg1rq6YroKvpYk+Is6vzP5C0g+B64CjKM7mp3zpZAvHxsZYtGgRACMjIyxevJjR0VFg\nYz9IE/PVPpj5bg+a35/J5qFHr5dfvroyD6fQ6+VxvLdl/oMfXMn55x+fTTxtmJ9YVt/28tq/hZjv\nz11T8axcuZK1a9cCMD4+znQW7DY5SRcDVwIfAa5h8yb6bwCrI+Lovtdlewbf6/U2JHq+cv3VW2dc\ndeYL8s1ZnaQeEaNNh9EqXclZrp/NLnwuof7vs7oM/HGxkramOIP/RET8Y3nL3KkRcXJl/c3ACRFx\nRt9rs63g65RrbLnGBXnHVpcu7GPdupKzXPcz17i6YsGb6CV9BPgqcD3wJ8D7gG2Aiec8nQK8R9Iv\ngV9R9NGvAz5fx/ubmZnZpuq6D3434GyKi+zOBe4Bnh0R1wNExIeAjwGfAC6luBDv4Ii4q6b3H4hq\nH4zNzPlK57Ho56LXdACt489mujbmrK6L7P5uFmWWAkvreD+zYeWx6M2sLh6LviG5xpZrXJB3bNac\nrowdkOvxn2tcXTHwi+zmwxV8s3KNC/KOzWyh5Xr85xpXVyz4w2a6oo19ME1yvtI5Z+mcs3TOWbo2\n5swVvJmZ2RByE31Dco0t17gg79jq0pX+ZEuX6/Gfa1xd4T74DOUaW65xQd6x1aUL+2hzk+uxkWtc\nXeE++Jq0sQ+mSc7XXPSaDqB1xsZ6TYfQOv5spmtjzlzBm1mrLV8+cxmzLnITfUNyjS3XuCDv2OrS\nhX2sW1dylut+5hpXVyz4WPS5CgST7nbzovJfMzOzug11E72I4qdlTVNvxYratqUOVO5t7LNqmsei\nn4te0wG0jj+b6dqYs6Gu4M3axmPRm1ld3AffkFxjyzUuyDs2a05Xxg7I9fjPNa6u8H3wGco1tlzj\ngrxjM1touR7/ucbVFb4PviZt7INpkvOVzjlL55ylc87StTFnruDNzMyGkJvoG5JrbLnGBXnHVpeu\n9CdbulyP/1zj6gr3wWco19hyjQvyjq0uXdhHm5tcj41c4+oK98HXpI19ME1yvuai13QArdOlseil\nuqZebdvafvumszIYbfw+cwVvZq3WlbHoaxyzq9bt3XZbs3mxqbmJviG5xpZrXJB3bHXpwj7WzTlL\n55wNj86ORW/pPH6/mdlwcBN9gjb2waSqc/z+Osfu78r4/R6Lfi56TQfQQr2mA2idNn7/D/0ZvDI9\nG+3KhSmWxmPRm1ldhroPvm5d6LfKeR9zjs2a47ED0jlnw6Oz98HXrQsVTM77mHNsZmZN8H3wtek1\nHUCrtLHPqmnOWTrnLJ1zlq6NOXMFb2ZmNoTcRJ+gC03EOe9jzrHVxX2jZpbCTfQ1OemkpiOwYbd0\nadMRmNmwGGgFL+lYSddJukfSf0vaf5DvP1+jo72mQ2iVNvZZNa/XdACt06Wx6OvinKVr4/fZwCp4\nSa8ETgH+EVgMXAJ8S9KjBhXDfK1cubLpEAairodQHHDAyhofjtGVsQO6cYzVafly5yyVc5aujd//\ngzyDfytwVkR8OiKuiojjgBuBNw4whnlZu3Zt0yEsuHofaLG21u1146EWw3+M1c85S+ecpWrj9/9A\nKnhJDwGeDnynb9V3gP0GEYOZmVmXDGqo2h2BLYCb+5avBnYZUAzzNj4+3nQILTPedACts88+402H\nkA0ljDMtze7qxFzv0Bm88aYDaJ02fv8P5DY5SbsCq4DnRcT3K8vfDxweEXtUlvkTaGZmNktNPy72\n98ADwM59y3em6IffYKpAzczMbPYG0gcfEfcBlwEH9616IcXV9GZmZlajQT4u9qPAZyT9F0Wl/gaK\n/vdPDTAGMzOzThhYBR8R50h6JHAi8KfAz4G/jIjrBxWDmZlZV2Q3Fr2ZmZnNn8ein4Skp0taL+n7\nk6xbP8n04ybizMkMOXu+pIsk3SLpLkm/lvRZSQ9vItYmSVo2xTF0Sbl+vaRDp3jd1wYfcbMq+Tqx\nb/louXyHvuVfLZcfNNhI8yJpN0mnS7pe0h8krSrnd+srt7Okj5efyXvLct+U9BdNxd4USTtJ+mQ5\nnPq9km6SdGH/sVTm7D5Jv1HKvZwNcAU/udcClwLPlrTHFOt3qUwvGGBsuZo0Z5L2As6nGIN1FHgy\nxfUXa4GtBh9m4wK4gE2Pn12Av5zF67rY3BbAvcDbJe04XUFJfwr8BfCfFMdjJ0l6LPDfwF7AkcDj\ngVdTfPYulfSYstwi4McUFzu/C9ib4rvsG8Bpg447A+cCzwCOAZ4IvBj4FrBDX7mjgKuBEeDPBxlg\nqkFeZNcKkrYB/q6c3gq8Bnh7X7G1EbF60LHlaoacHQz8PiLeVnnJOHDhIGPMiIA/zOH4UTl10Qpg\nd+B9wFumKTdGcbfO24CLJe0QEZ0Y4LjPJ4A/AgdFxL3lslXlmeivyvUvBj4JrAeeERF3V15/laTP\nDDLgpkkaAfanyNmKcvH1FD+U+h1D8VyVp1N8150/kCDnwGfwm/tb4PaIOB84HThSUv8Poa5+0U5l\nspxtUa67EdhJ0mhTwWXIx8/siaISehfwBkmPm7RQ0VR6DHBGRFxCUZEdMbAoM1F2Wfw58IlK5Q5A\nRNxDUan/RVnukLLc3f3biYg7BhFvRu4sp5dJmrJlUdJzKX5sfg44A3jpTC1LTXIFv7nXAGeW//4P\niibCl/WV+YykdZXp7wYaYX4my9lflfNfAj5PcUZ1U9lH+r9y/lAMwCF9x886SSc3HVTGIiK+BfwA\n+KcpyjyfYuCss8v5MyiOy655IsWPoiunWH9luf7xlfnOi4g/UrQAvRpYK+kSSR+W9Ky+oq8BzomI\ndRFxKXA5RZN9llzBV0h6AvA/gbNgwx99OZt/UZwA7FOZOnfx04SZchYR6yPiGIpfvScAv6Vovv9l\n2T/fRd9l0+NnH+DDs3hdF/vgYWOLxzuBl0t6+iRlXgt8oXI2+hngCZN8QVvBrUh9IuL/ALsCL6Ho\ne98P+JGkdwNIegRFa+X/V3lZ1j8k3Qe/qddSPBTn2srFkYLiqtSIuKFcdlNEXNtAfDmaLme7R8Qq\ngIj4HfBZ4LPlFdFXU1T0Rw884ubdM83xsw7YbpLlI8DtCxdS/iLiUknnAh8C/mFiedl/+jfAlpKq\nx9MWFMfnfw000Gb9muKH4JOBr0yyfi+KLo9fleX2mqJcJ0XEHyiuD7oQ+AdJZwBLJH0EOBx4KPDd\nvovnHyRpv7JrKCs+gy+V/exHUfT19Z9d/Yyif88qZpGzSSvviFgL3ARsO5hIszPdmfhVFFfyblBe\nz7BPua7r3gM8l+Jq+Qmvongy5VPZ9Bj8e+CVkh466CCbEhG3At8Gji0vft2gzMObgG9FxJqy3Jsl\nbfY5LH80WdGFsQWwNcWZ+qlseowtBr5JpmfxPoPf6EXAIyku0llTXSHpCxS3dv3DZC/ssBlzJulm\nig/BecC1FB+UI4GnAF3td95a0s5s2kz6QETcQjGk81mSLqc4i3go8P9QnMGfPvBIMxMR10g6neJq\n+qDI4WuAL0XEFdWykn5N0fXxSsoupI54M8Vw4BeWrWW/puhz/yeKnL25LPcmiusa/lvS+yhGFxVw\nAMWP9scMOO7GqBhl9UvApynysI7ih/Y7gIuAxwL7AkdPcpx9Bvi0pLdExJ0DDXwGPoPf6Bjg4v6K\nqvRl4DGSXjjgmHI3Xc6+BCyiaG5+KMV9tT+n6H9+LnBERJw9yeuGXQAHUdxd8LvKdBlARHyBouXj\naIpxBb4F/Anw3I7emjnZ/f8fAO4v//04ih+QX97shcVDrr5KpmdXC6Xs/nkGxQVgnwGuobjq+3Lg\nmRHxm7LcdRS3el0A/AvwU4rK7GXA8YOPvFHrgB9S/HDsAb+g+EH0WeAwiq6eqyPiF5O89hsUdelh\nA4k0gYeqNTMzG0I+gzczMxtCruDNzMyGkCt4MzOzIeQK3szMbAi5gjczMxtCruDNzMyGkCt4MzOz\nIeQK3symJGmRpPVTPODFzDLmCt7MAJDUk3Rq3+LfArtQjHI26HjGJb1t0O9rNiw8Fr2ZTSki1lM8\nyKWRt2/ofc2Ggs/gzVpE0tsk/UrSvZKul/TP5fK9JV0o6W5Jt0o6q3x+9cTrlkn6mqS3SFol6TZJ\nZ048cUzSMuB5wJvKJvn1kh7d30QvabScP1DSf0q6S9Klkp7WF+d+kr5brl8l6ZOSHl5Z35P0CUn/\nLOkWSTdL+rDK53BK6lE87OTD5fs9sLCZNRs+ruDNWkLSycCJFA/B2BM4FPhN+bjPbwN3AM8E/hrY\nDzizbxPPpXj+9wsonrD21xQP1wA4juJhG2dSNMnvAqyaJpx/pnjS1tOBWykeZjIR595lPP9B8QjX\nQykeCNMfz6uA+4DnUDzh7PgyLsrYVgFLy1j+dJpYzGwSbqI3awFJD6OoAN8SEcvKxdcBl0p6HcUT\n+46IiLvK8n8PrJD0uPLpYgC3A2+I4glTV0n6EkVl/8GIuEPSfcDd1afWlSfUk3lfRHy3LPMB4PuS\ndo2I3wFvB74YER8ry14j6Vjgx5J2jIjfl8svj4gl5b9/Xe7HC4AvRMSa8qx9XUefomc2bz6DN2uH\nvYCtKB7n2W9P4KcTlXvph8D68nUTrohNHx95I8WjaOfiZ33bobKtfYFXS1o3MQHfp+hTf3xZJvq2\nMd94zKyPz+DNhsNUp9rVCv2Pk6yb64/8+yv/nniPiW0JOAP4GJv73RTbmG88ZtbHFbxZO1wJ/AE4\nCLimb90VwNGSHhYRd5bL9qOoLK+slJvpqvT7qOc74cfAUypdA3N1H7BFDfGYdZJ/LZu1QESsAz4O\nnCxpTNLjJT1L0hsoLnC7G/h3SU+R9Dzg34Bz+yrZKTvUS+PAsyQ9RtKOmqYDfgb/Um7nNElPk/QE\nSS+W9Km+WGYTz/Mk7SppxznGYtZZruDN2uPdFJXn+yjO2r8M7BYR9wB/DjwC+C+Kq9d/ABxTeW2w\n+Rl8/7KPUJw1XwHcDDyqUq7/df02LIuIn1PccrcI6AErKa66vykxnveXMVxTxmNmCbTpNTdmZmY2\nDHwGb2ZmNoRcwZuZmQ0hV/BmZmZDyBW8mZnZEHIFb2ZmNoRcwZuZmQ0hV/BmZmZDyBW8mZnZEPq/\nl+NeWNMJWR4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c27e8d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# box plot of beer servings grouped by continent\n",
    "drinks.boxplot(column='beer', by='continent')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x000000001C27B128>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000000001C86DC18>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x000000001CB19668>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000000001CB38208>]], dtype=object)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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4vwD4I7AKONnMVmfyLQWOBWYDPwGOM7ObU8d3BD5NiAe/E2Fd/fuyS+V8kt30\n6acOPqbJfIOoxTt4x6kGzdp6Ozb4Q4GzgZcTfMo/AVwlaXbqAicBHwTeDxwMrAeulLRLqpxlhB8K\nbwNeQQg7e5mkrswDGBurd6PYaRGLzScWHYF62QJS1MsWkKJetoC+I6bPtWvJx7XkU7oN3swOT6cl\nHQU8QIgqd7mCM+3FwOlmtjLJs5DQyb8dOFfSTOAYYJGZXZ0q5y7g1aTixBdFrVZ0iY7jOI7TP0wn\nHvyfEqLKzTez6yTtC/waONjMrk/luwz4g5ktkvQqgmva3c3svlSem4CLzWxpap8P2U0TH6KfHoOo\nxYfoHacadDpEn+WzwA3Aj5P0nOT/uky+9aljc4An05176pw9pqHBcRzHcZwmTGmZnKTPEIbm57f5\ns3taP81rtRq1Wo25c+cya9YsarXaNgHvgZbpxr5283czPTo6yuLFi0u7frYumuWHOvV659c78sgh\nNmwI5SVXHC+/gTTU5PgQs2fDN785/ffbbvq1rx0lWJm6W/+77gobNkz+fgPLkGpNjtfZZRfYtGnb\n8huvV61axdq1a+knOm3vsbQvgGXLlk3r+6qs9t6rdFaTfx+H9HQ+L6Ojo2zcuJGxsTFGW/lknyxQ\nfHYDziQMzb8gs39fYAsh7Gt6/+XABcnrVyV5dsvkWQ0syezLi2k/ZUZGRgoppwh6paVV1bWjo6Dq\nj0pLK2K5P2bF1UvSjtpu32VtRbT3Krb1dnAt+QyalmZtvS0bvKTPAm8BDjOz2zLHlHT8Z5nZ6cm+\nGYTh9xPM7Lxkkt16wiS7C5M8exMm2R1uZlemyrN2NDkTickGH5OWWOhl3boN3nGqQUe+6CWdA7wD\neBPwgKSGXX2TmT1sZiZpGXCKpFuB24FTgU3A1wDM7AFJXwLOkLQeuB/4DHAjYfKd4ziO4zgF0s4k\nu/cCuxAc0/w+tX2okcHMziAM4Z8D/JQwcW6BmT2cKmcxsBK4CPgh8CBwRLd+vqdtP2UTi5ZYdIBr\nmYyYtPQLMdWZa8nHteTTbS3trINva6a9mQ0Dw02ObwaOT7auMzpaDfe0juM4jpPHQPmiT+Ouassr\nIzYtrejVZ8Vt8BNxG7zjdEbR6+AdZ6AYnnTcyXEcp38ZqA6+Xt/6NDY8vDUefNkml1hsPrHogLi0\nxOT/Pa566Q9iqjPXko9ryad0G3w/kQ4LOzZWvSF6x3Ecx2ngNvgBIia7d0xaBu06boN3HKdBRzZ4\nSa+U9G1Lw+r1AAAgAElEQVRJayRtSSLFpY8vT/ant+syeXaUdJakeyU9JOlSSXt19raa4zPoHcdx\nnCrTjg1+Z+AXwL8AjzLRv7wBVxICyjS212Xy9DQWfKDevaKnSCw2n1h0QFxaFi6s9+Q6hsLjd5Ot\n3uI4UijHGSemz5Jryce15FO6Dd7Mvgt8F8LTek4WAZvNbH3e+WXEgnecqbBoUW+uI6z1EH2I9NO8\nHE0zipPjOJViSjZ4SZuA48zsP1P7LiC4sd0MbAS+D3zUzO5NjrcdCz7ZP2WbXHCH35pBt/XFZPeO\nSUssuA1+Im6Dd5zO6MgXfRt8D7gEuBPYBzgNuEbSvMR7Xddjwed9QQxa5+A4juM4U6HjDt7MLkol\nV0u6njD8/nqC7/kpU0Q8+IULATz+cDqdtvdMlh/qyShxZ9drxC+f/LjHh85LtxsfOlu/jdceD751\n/m6mPR58fjqrydt7SEcTDz55St4EvLONfHcAH7YpxoI3jwffETHFYI9JSytiuT9mHg9+OlSxrbeD\na8ln0LQ0a+sd2+Bz8uwOrAHeZWZfmUos+OSYTUWTs5Wo7N5tzotoSQ8+C+6Lvjy8vTtOZzRr6y07\neEk7A89Pkj8CPgn8N3AfIa77MHAxsBaYC5wO7AXsb0m4WEmfB44AFrE1FvxMYF62dfd7gy9zwl9M\nHXxMWgbtOt7BO47ToNNgMwcDP0+2GYQO/efJ/yeBFwGXArcBy4FbgJdbibHgoby1jnnDJDCSNzTZ\nc8qqkzxi0uI+E/qbmOrMteTjWvLptpZ21sHXaf5D4PA2yuhpLPjYWLiwdR7HcRzHKRL3RT9AxDQs\nHpOWQbuOD9E7jtOgIxt8rymqwVdxHXxMnWpMWmK6ThHMng3339/qWt7BO04V6NQG36fUyxYwTi9t\nPs3dmNdbuTln9uze6Oxlney6a/P3DK3rZdddO9cRFrg136DeMk+rzr1qVMmmOhVcSz5V0jLAHXz1\naN15tM4ziJ3Hhg3N3/PISOt62bCh7HfhOI4zNXyIvkL0sk5iGqKPSUuvruND9I5TDboaDz7Js1TS\n3ZIekTQi6YDM8Z7Hg4+Jqk32cxzHccqn43jwkk4CPgi8n7Bmfj1wpaRdUtl6Hg++VzG+22F4uF62\nhIR62QLGqZIdbGrUyxbQd8R0/1xLPq4ln9Jt8Gb2XTM71cwuIfiUH0fBbdti4HQzW2lmq4GFwNOA\ntyd5GvHgTzCzq83sBuAo4MWEePBdoVcxvh2nSNxnguM4RdGRL3pJ+wK/Bg42s+tT+S4D/mBmi3oR\nDz52YpkP0EvfADHZvWPS0ivcBu841aCby+TmJP/XZfavTx3rejx4pz18LoDjOE516DgefBOm/bO8\niHjwjX1lx/sN6VGCJaM68aFjiwffTE878aFbvZ8y40M3qNc9HnzZ7d3jweens5rK1NPvn5eexYMH\n9iXY5edl8l0OXJC8rnw8+IULR8qWYGa9rZOY4sHHpKUVRd0jPB58KbiWfFxLPlHHg08m2d0NnGVm\npyf7ZhCG308ws/PKigdfRV/0MRGT3TsmLb3CbfCOUw26Fg/ezH4n6UTgFOBo4HbgVGA+sJ+VGA++\n376QB42YOtWYtEwst70+eKptwjt4x6kG3YwHj5mdAZwJnAP8lDBxboGVHA8+pvXEaTtUmSxaVC9b\nwjix1AmUqyU7pDYyMjLZULYzCf5Zyse15FMlLUXEg8fMhkk6/EmOVzoefCysWAHLl5etwnEcx+kF\n7ou+Qrgv+vK19AofonecalDRcLHxUNXJfq1CsMYSutZxHGcQ6csOvlV873ZifBcR37tdquiL3voo\n7nmVbHKDSEx15lrycS35dFtLX3bwreJ7Wxsxvj2+t+M4jjPI9KUNvt9sqrHYb2PyDdBv9R/LPWwX\nt8E7TjXoaB18r/EOvhr0W/332z30Dt5xqkHXJ9lJWippS2b7fU6euyU9ImlE0gFFXHsyYrKzxLIm\n3+skn5jqJSYt/UJMdeZa8nEt+fSTDf5WQuS4xnZg44Ckk4APAu8nOM5ZD1wpaZcCr18K7U34a368\nlxP+YsHjnjuO43SXQoboJS0F/s7MDsw5JuD3wOcy/urXE/zVn5vJ31dD9DFpcfKp4j3yIXrHqQa9\nWge/bzIEf4ekCyXtk+zfh+C+9opGRjN7DLgWOKTA6zuO4ziOk1BUB78KWAi8FjiWMER/naRdk9cQ\nIsylWZ86VjhVsrO0S1V90RvNbSj1NrzuGL15GI7ls9JPxFRnriUf15JP6b7o28HMvpdK3iTpx8Cd\nhE7/J81OzdtZq9Wo1WrMnTuXWbNmUavVtgl4H9g2PfE4LY43P7/dNNSp15vnHx0dbVleUXqapVes\n2NrJd6P8qaQb9OJ6hzGC2eTHR0dHGVq8uGl5hx02hPVA7+jo6LTOb7xetWoVa9eupZ9op7132r56\nlZ7u/Rv0dIMY9PT752V0dJSNGzcyNjY2fv5kdG2ZnKRrgFuATwO/AQ42s+tTxy8H1pvZ0Znz3Abf\nJfrNjlwU/XSPisJt8I5TDXruiz6ZRLc/cI+Z3QmsBRZkjs8HruvG9Z34icXhjuM4zqBS1Dr4T0t6\npaR9JP0FcDGwE7AiybIMOEnS30p6EbAc2AR8rYjr55EdGiqTeLTUyxYwTjz++WO6P3Fp6RdiqjPX\nko9ryafbWgqxwQN7ARcCzwDuBX4MvMzMfgdgZmdI2gk4B5hNmJS3wMweLuj6juM4juOkcFe1HRKT\nlla4L/pyy+glboN3nGowcL7ox93DdUohTn6q13kUgXfw3cU7eMepBj2fZNdtRItYsWbUW8SLVf4K\nva4Qi80nFh2BetkCxompXmLS0i/EVGeuJR/Xkk+3tfRlB+/0P+6L3nEcp7v05RB9TEOuMWlx8inC\nojN7Ntx/f+fl9Aofoi+H4PSqbBXx4fXSPQZuiD4mWrlBbWfrlRvUqtLCmtNWnn7q3J3yWL68bAVx\nEtGoeKXoaQcv6X2S7pT0qKSfSZo//bJabfWmx2fPLug99dF8gKr6om9NvWwB48RVL/1BTHW2cmW9\nlOtKamsri7GxemnXzhLT52VgbPCS/p7g8OY0oEbwYvddSc+aalmtnrbCU9loNE9krfwF94oVK+LQ\nAfHUSSAeLXHVS39Qdp3V61uXoD744Oj46172I2Y2YYMzc/b1jnS9rFhRTr3kUfbnJU23tRTl6KYd\nPghcYGZfStLHSzoceC9wSvGX21h8kdNk48ZYtMSiI6Y6Aa+X/iLvSfQDH/jANuny7Po9un9tPI2/\nlr8AfaBlvqInAOWPFBzK8HDQMjzcuGz379FkoxZlfF7K0NKTDl7SU4E/A87IHLqCrsWEH+tOsdNg\nbGysbAkJY2ULGOdb3xqLxulOTPUSz2clXrJfgNIizJaXoiV8aX8TOCzZM8bwcOjkh4dHgCO70nm0\nZ9ZbhFjVNMfs2dDxYGam48pTVmPjxHGybIdXQD3tuits2NBKzSKCt/R8KYVNqG2jXiYqmXheJ/XS\nqyf4ZwDb09OY8L0bhmn9Y3qUFSsmP1rUfIDWxDM0deON8WjZY494tMQ0fBgFbTypHgSgJg2sQYcd\nyMTOAyZ+bY8Cs5LXfwvYhLdQRAfSzluRRnsTpXJCHeTds1/n7N16XiE/NAj3p9V7rtVGadbMCpuq\nMOGHaH7B2U9u3z3BT5WiJoOUOalkIpNr2bChwA9VKxUR1YlryScmLb2giPfbVgk9q9fm16lme88L\nO7JVW5F10k45reqlzGor8p71qoP/A/AksEdm/x7APekd/bB213GcYvD27jjdoyez6M1sM3A9qZjw\nCa/BY8I7juM4TuH0coj+M8CXJf0voVP/Z4L9/Ys91OA4juM4laBnHbyZfV3SbsCpwJ8CvwRe14gZ\n7ziO4zhOcUTni95xHMdxnM5xX/SO4ziOM4B4B+84juM4A4h38I7jOI4zgHgH7ziO4zgDiHfwjuM4\njjOAeAfvTAlJWyQtaTPvUJL/ld3W5ThOZ0haLunOsnU4xeEdvDMdprK2cpu8kl7X7g8Ex3F6ijG1\ntu1Ejq+Dd6ZEEvr3STN7so28AnYAHrfkgybpbOB9ZuY/Lh0nIiQ9hdAnPF62FqcYoowm58RLEleg\nKZJ2NrOHk049L7//qnScyDCzJ8rW4BSLP0VVAEm7SPq0pDslPSZpvaQRSa9Ijtcl3SLpIEk/kPSw\npLskfSinrG1s8JIWJfsOk/Q5SeuATcmxbWzwkpYD7wsvtSW1PbsX9eA4VUDSgUm7OjK1b79k368y\neb8saSx5PcEGn5zzBUlvknRT8v1xk6TX5lz3TyX9h6S1Sb6bJf1zl96m0wb+BF8NvgC8BTgbWA3s\nCvw58GLgB0memcD3gG8CFwJvAj4laXszOyNTXt4T+FnA/cD/TcrK44uEOASvAd6R2v+HKb4fx3Em\n5yZgA/BKQnsmeb0FeK6kOWa2Ntn/CuD7qXPz2vbLgSOAzwMPAccDl0h6tpndDyDpmcAqQpD3s4H1\nwKuBz0vazcw+UeD7c9rEO/hq8DfAuWZ2QpM8c4CTzezfACR9EbgK+LikL5rZgy2usQkYMrMtk2Uw\ns1WSbgdeY2Zfm9pbcBynHczMJP2I0Kk3eAXwXWAo2f91Sc8Cng1cm8qnnCJfCBxgZncASBoBbgT+\nATgnyXMaYb7NgWZ2X7LvXEnnAqdIOtvMHiji/Tnt40P01WAj8DJJezbJ8yRbGytJR30O8CfAYW1c\n47xmnbvjOD3lh8CLJT0tSb8CuIbwlP3K1D7YOoo3GSONzh3AzH4JPAjsA+OTad8MXJ4kn9HYgCuB\nnYC/6PwtOVPFn+CrwYeBFcBvJd1AGIr/spml7XHrzOyhzHm3J/+f08Y1ftO5TMdxCuIHhAe4+ZJu\nIrTh7wO7EMx1EDr4dZnvgTx+m7NvAzA7eb07MAt4V7JlsSSP02O8g68AZnaxpB8AbwQWEGxoJ0pa\nZGYXFnSZRwsqx3GczvkZoU0eSphzswm4gTA/Zqmk2YQO/odtlDXZktjGcH5jJPhrwPmT5L25jes4\nBeMdfEUws3XAuQS72EzCUN0wYUIdwBxJTzOzTanTXpD8HytSSoFlOY6Tg5k9LqkxHD8T+FFim18F\nPEH4sb8/8O8FXO5ewg+IHczsmgLKcwrCbfADjqTtkg59nGSyyxjbznbfDjgufV6SfgQYKVDSw0n5\nswos03GcifwAmEdYtXItgJk9Sni6P4nwBH5t5pwp/wBPnF5dDLxJ0ouzxyX58HxJ+BP84PN04G5J\nFwO/IEyO+UvgtYSlbQ3WAv+SrElfTVgmdyhhZv0miuOnyf+zJX2P8DTxbTN7pMBrOI4TOviPA/uy\nbUd+LaGDf8DMbsyckzeLPo9svo8QZuj/WNJ5hCH52UCN8F2y05SUO4UwpSd4SScnjg/OSu1bnnFa\nskXSdZnzdpR0lqR7JT0k6VJJexX1JpymPExYl3ogcCpwJvBnwIeAD6TybSTY518EfIowPH9iY9lc\nC5r96s8e+yawDPgrwsS/rwLPaOMajuNMjR8TfkA/Cvxvan9j1vyPMvmn4ot+m3xmdi9hpvx/EDr0\ns4DFwDOBD05JtVMYbfuil/QywiSKB4Frzez4ZP8FwJ7AUansm81sY+rcLwBvAN5JcIbyGcKsy3m+\ntKp8JNWBZ5rZAWVrcRzHcYqhrSf4xIb7FeBowvKIbQ4TOvT1qW1j5txjgBPM7Gozu4HwY+DFBE9H\njuM4juMUTLtD9OcC3zCz7zPR9mKEtZbrJN0m6dzMpIp5BA9HV4yfYLYGuAU4ZPrSnYJp1/bmOI7j\n9AEtJ9lJOpYwSePtya7smP73gEuAOwmejU4DrpE0L4k8NocQXvS+zHnrgD060O4Uh8eBdhzHGTCa\ndvCS9gM+AcxPxf8Wqac9M7sodcpqSdcDdwGvB1YWK9fpBmbWjitax3Ecp49o9QT/csIM59XB3TAA\n2wOvkPQeYGczezx9gpndI2kN8Lxk11pg+ySiUPopfg4T12AiyZ8kHacAzCx6s4u3d8fpnMnaeisb\n/ErCsqmDkq1GcJJwIVDLdu4w7tRgL+CeZNf1wOOEJViNPHsTIhRdlz0/EdvxduihhxZSziBpiUWH\nmfGc58SjJaZ6KUpLPxFLncV0/1yLa2l3a0bTJ3gLHs+2CfEn6RFgg5ndLGkXYCnBi9FaYC5wOsG+\nvrJRhqQvAWdIWs/WZXI3EsKRdoUZM2Z0q+gpE4uWWHQAPOUp8WiJqV5i0tIvxFRnriUf15JPt7VM\nx5NdekLWE4Qn/KMI69rvIYQkfLOZPZw6Z3GS9yKCR6OrgHdYq58fHTBnzpxuFT1lYtFSto56PWwA\nv/nNHJYuDa+HhsJWFmXXS5qYtPQLMdWZa8nHteTTbS1T7uAtNSHLzB4DDm/jnM2ECGbHT/V602XR\nokW9ulRLYtFSto50Rz42tmi8gy+bsuslTUxa+oWY6sy15ONa8um2lrY92fUKSd18sHciYelSoung\nBxFJWJ9MsvP27jjTp1lbH9hocvXGWHAExKIlFh0As2bVy5YwTkz1EpOWfiGmOnMt+biWfLqtZWA7\neCduarWyFTiO4ww2PkTvOAOID9E7TjWo5BC94ziO41SZge3gq2RnaZdYdIBrmYyYtPQLMdWZa8nH\nteQTlQ1e0smStkg6K7N/qaS7JT0iaUTSAZnjO0o6S9K9kh6SdKmkvYp4A47jOI7jTKRtG7yklwFf\nAx4ErjWz45P9JwEfBRYCvwI+DswH9jOzh5I8XwDeALyTrZ7sZgHzzGxL5jpuk3OcDnEbvONUg45t\n8JJmAl8BjgY2pPaL4KXudDNbaWarCR3900jCyybnHgOcYGZXm9kNBM93LwZePe135TiO4zjOpLQ7\nRH8u8A0z+z6pULGE+O97AFc0diTe7a4FDkl2zQN2yORZA9ySylM4VbKztEssOsC1TEZMWvqFmOrM\nteTjWvLptpaWrmolHQvsS/JEzlY/9BBCvkIILpNmPbBnKs+Ttm2o2MY5e0xJreM4juM4bdG0g5e0\nH/AJYL6ZPdnYzbZP8ZMxbcNarVajVqsxd+5cZs2aRa1WYyhxYt74xdNv6QZl6hkaGoqmPmJLNyhb\nT2PfdPTX63VWrVrF2rVr6SeKaO8N+vX+eXvvbbpB2Xoa+6Zy/ujoKBs3bmRsbIzR0VGa0XSSnaRF\nwPnAk6nd2xM67ycJkeRuBQ42s+tT510OrDezoyW9ihA9bvf0U7yk1cDXzWw4c02fdOM4HeKT7Byn\nGnQyyW4loRM/KNlqwM+AC5PXtxPiwC9IXWwGYRb9dcmu64HHM3n2Bl6YylM42V9qZRKLllh0gGuZ\njJi09Asx1Zlryce15NNtLU2H6M3sAeCB9D5JjwAbzOzmJL0MOEXSrYQO/1RgE2FJHWb2gKQvAWdI\nWs/WZXI3Ep7sHcdxHMcpmCn7opc0AvyysQ4+2bcEeA8wG1gFHNf4AZAcfyrwacJEvZ0IHfv7zOzu\nnPJ9yM5xOsSH6B2nGjRr6x5sxnEGEO/gHacaVDLYTJXsLO2ybFm9bAnjxFIn4Fr6nZjqzLXk41ry\n6baWge3gnYm0WFHhOI7jDBA+RF8hli4NmzP4+BC941SDZm29pSc7p7+p18MGMJzyODA0FDbHcRxn\nMBnYIfoq2VmaMTS09cl94cL6+OuyO3e/P/nEpKVfiKnOytIiqa2tLPwe5VO6DV7ScZJulPRAsl0n\n6XWp48uTGPHp7bpMGR4P3nEcp0uY2YRtZGRkwj6nWrS0wUt6A/BHghOb7YBFwIkE97Q3SrqAEFjm\nqNRpm81sY6oMjwcfAfV6+U/uTm9wG7zjVIPC18FLug/4iJmdJ2k5sJuZHTFJ3pmE6HKLzOzCZN/e\nwF3AX5vZFZn83uAdp0O8g3d8Um01KGwdvKTtJb0NmEGI+Q4h8Mx8Sesk3SbpXEm7p07zePCRaIlF\nB7iWyYhJS78QU53FpGV4uF62hHFiqpcqaWlrFr2kA4EfAzsCjwJvNbPbksPfAy4B7gT2AU4DrpE0\nz8w24/HgHcdxHKfntLtM7lbgxcBM4C3Af0k6zMx+ZmYXpfKtlnQ9Yfj99YRodFPG48F7fOgq3p+0\nhunor9c9Hny/3r/upL29D+LnpbB48JOeJF0JrDGzoyc5fgfwBTP7lMeDd5ze4zZ4RwKv2sGnG77o\nt5/s3MT+vhdwT7LL48FHoiUWHeBaJiMmLf1CTHUWkxaoly1gnJjqpUpaWg7RS/okcBmwBngaIeTr\nocDhknYGhoGLgbXAXOB0gn19JXg8eMdxnDJYuLBsBU7ZtLMO/gLgMMJkuQcIHfOnzOxKSTOAbwEv\nIaxrvwe4BvhYOta7x4N3nN7iQ/SOUw08HnzJuIOZiXiddBfv4B2nGng8+JJZvrxetgTA62QyYqqX\nmLT0CzHVmWvJx7Xk020tA9vBO47jOE6V8SH6LlGvhw1CmNYlS8LrKodp9TrpHT5E7zjVwOPBl0C2\n03Kf0F4njtNL3Be9M7BD9DHZWcbG6mVLALxOJiOmeolJS78QU53FpMV90edTJS0D28HHRK1WtoL4\n8DpxHMfpLu2sgz8O+CeCExuA1cBpZvadVJ6lwLHAbOAnwHFmdnPq+I6EdfBvI6yDvxpfB18ppPbM\nwX7vi8Ft8I67qq0GnS6T+x1wIsGZzTyCI5tvSTooKfwk4IPA+4GDCbHfr5S0S6qMZcCRhA7+FcDT\ngcsk+QhCRTCzbTawCfv8i37rJETHcZxOadnBmtm3zex/zOwOM/u1mZ0KbAL+XOGxbDFwupmtNLPV\nwEK2urRF0kzgGOAEM7vazG4AjiJEp3t1d95Wtews7RKLjkC9bAHjxFQvMfkH6Bdiun8xafE2lk+V\ntEzpCVrS9pLeBswAriXEf98DuKKRx8weS44dkuyaB+yQybMGuCWVx3EcxykQ90XvtLUOXtKBwI+B\nHYFHgX8ws8slHQL8EHh20mk38p8P7Glmh0t6O7DCzHbIlHk18Csze29mv9vkKoAv4dlKN/wDuA3e\ncapBEevgbyUMqc8E3gL8l6TDWpwz7VZbq9Wo1WrMnTuXWbNmUavVtgl4D3i6z9NLl8alp+x0oz7G\nxupJxz618xuvV61axdq1a+knvL172tPtp0dHR9m4cSNjY2OMjo7SlLyJTq024ErgAsIQ/RZgXub4\n5cAFyetXJXl2y+RZDSzJKduKYGRkpJByiiAWLbHoMHMtk7Fw4Ugh5STtaFrtu5dbEe09pvvnWvJx\nLfkUoaVZW5+uJ7vtge3M7E5Ja4EFwPUASQjZ+cAJSd7rgceTPBcmefYGXghcN83rO23gS9P6D/cP\n4DhOUbSzDv6TwGXAGrbOjj8RONxCTPgTgVOAo4HbgVMJHfx+ZvZwUsbngSOARcD9wGcIw/3zLCPA\nbXLdw9fFVge3wTtONeh0HfwewFcIdvirCLPiDzezKwHM7AzgTOAc4KdJ/gWNzj1hMbASuIgwKe9B\n4Ahv2Y7jON3BJ7E67ayDP9rM5prZDDPbw8wWNDr3VJ5hM9vTzHYys8Ms5cUuOb7ZzI43s2eY2c5m\n9kbL8WJXJOnJR2UTi5aFC+tlSxhn0aJ62RLGieX+QFxa+oWY6iwmLe6LPp8qaXFPchVi0aKyFWxl\nxYqyFTiO4ww2Hg/eKQWfD9Bd3AbveBurBp3a4B3HcRzH6TMGtoOvkp2lXWLREaiXLWCcmOolJi39\nQkx1FpMWb2P5VEnLwHbwMdHK2ZDjOE7RuC96p5118CcTQr2+APgjsAo42ULkuEae5cA7M6euMrND\nUnnaigk/iDa5WPyux6ID4tIyiLgN3nGqQac2+EOBs4GXE9zOPgFcJWl2Ko8R3NfOSW2vy5TjMeFL\nZni4bAVbialzj2jEznEcpzDaWQd/uJmtMLObzewmQiz33dk21KuAzWa2PrVtHD9YQkz4su0s9frW\np9Th4fr463JllXrxbSj7/qSJKQZ7TPXSL8RUZ64lH9eST7e1TMcX/dMJPww2pPYZMF/SOmAj8H3g\no2Z2b3I8Nya8pEZM+CsYMNKhPletiuuJ1XEcxxl8prwOXtLXgecCL20YzyT9PfAwcCchwtxphIA0\n88xs81Riwg+iTW5oqOwn94Cvi91Kvb71nhQVgz0m3AbvONWgiHjwjYI+Q3jinp9ulWZ2USrbaknX\nA3cBryf4oJ8SgxYfesYMgHj0eDqkww+vOmNj28anr9fj0DeVdOO1x4P3dCO9dCkMDcWjx9PFpLsS\nD54QUOZu4AVt5r8D+LBNMSY8AxIPfmTEbMmSsMHI+OsyZRUVa7wIXEs+RX1u8XjwpRCTFhgpW8I4\nMdXLoGlp1tbbeoKX9FngLcBhZvarNvLvDuwF3JPsqlxM+PRQb3hCLE9Lg9h80S9fXraKgMdgdxxn\nEGlnHfw5wDuANwG3pA5tMrOHJe0MDAMXA2uBucDphA5+f5tiTPhBtMn5mu+J+HyA7lIlG3wwqRQi\nZ6DwNlYNOl0H/15gF4Jjmt+ntg8lx58EXgRcCtwGLCf8EHi5eUx4wL98HKebxDCB1XFipJ118NuZ\n2fbJ//T2r8nxxyysld/DzHa0EDv+GMt4qLMex4SvR9Xq62ULALxOJiOmeolJS78wNlYvW8I4cd2/\netkCxompXqqkZTrr4B3HcUolvcxxxQqYOze8HpRljkXgvugdjwdfIWKaCxCTlkGkSjZ4/yw5Vcbj\nwTuA+6J3HMepEgPbwVfJztI+9bIFjBNPnbiWfmfWrHrZEsaJ6f65lnyWLauXcl1JbW1FMrAdvOM4\n1cD9GDhToZXzt26R54gGRvKcPxVGIfHgk3xLgWOB2cBPgOPM7ObU8crGg48FXxdbHapkg3ecqRDT\nnI0ivpM79UXfiAf/U8IT/78S4sEfYGYbkgucBHwQWAj8Cvg4cKWk/czsoaScZcAbCB18w9HNZZLm\nmdmWab87x3EcZwIxdWRlk151kZ6LNPCrLibzYTvZBuwMPAG8PkmL4JL25FSeGQRHNv+UpGcSnv7/\nIUfQxiMAABrNSURBVJVnb4KTnAWZ8ttzwNuCQfM3XAQx+VyPScuZZ46ULWEc90U/dWJpX2ZxaXFf\n9PnE9N1TxD1q1tanY4PPxoPfB9iDbWO9PwZcS4g8B5PEgyd4vGvkcbpMbL7oY6Esm5zjONWm274K\nOo4HL+kQguvZZyeddiPf+cCeZnZ41ePBOxOJaT7AIA5lug3eiamNxcSgxS7oejz4Jkzr49XP8aHb\nXebQqL6y9ZaVhvKvX68HN6dhNCEcnzWrTq1Wfv1M/f14PHhPe7pfvn86Sfc0HjywLyHW+7zM/suB\nC5LXlYsHnyYWLbHoMIvLPhiTTc5t8FMnps91TFpiamMx1cugaWnW1tuywSfx4P8eeJVNjAd/JyFM\n7IJU/hnAfLbGek/Hg2/kGeh48I7jON1i113DEHyzDVrn2XXXct+H0106jgef5DkROAU4GrgdOJXQ\nwe9nHg8+Gnppa951V9iwoXW+ZsyeDfffX4yeZgyaTQ7cBj/oFGVfdzt9/9OsrbfTwW8h2NKzBSy1\nJGRskm8J8B6Co5tVTHR081SCo5u3ExzdXEVFHN3EMomrl425GAcO/uUzXbyDH2y8gx8MiugbOgo2\nYy3iwafyDZvZnma2k5kdlu7ck+OVjQc/PFwvW0JCvWwB48R0f1xLfxNTnbmWfFxLPt3uG9wXveM4\njlMZquT3wuPB94BYhsF8iL46+BD9YOND9NMnFpMpxOGL3nGcLjFVvwmO4zjtMrBD9DHZWXpl+261\ndAbq0Sybien+lKklu251ZGRksvXiziT4Zykf15K+/tYn9+Hh+vjr8quouwL8Cb4HdNvfcIMNG5oP\n97SzHKzNB0rHcZy+IR01bmwsniH66HzRdxu3yU2fmOzeMWnpJ4qyD7oNfrCJqZ32GzHZ4Iugo2Vy\nkl4p6duS1kjaImlh5vjyZH96uy6TZ0dJZ0m6V9JDki6VtFdnb8txBo90rGrHcYpn0JxaNaMdG/zO\nwC+AfwEeZWIAGQOuBOakttdl8iwDjgTeBryCEHL2MkldmwOwbFm9W0VPmbLtTw1i0QGuZXLqZQvo\nO2K6f64ln5i0xNTGul0vLW3wZvZd4LsQntZzsgjYbGbr886XNBM4BlhkZlcn+44C7gJeTSpGfJFU\naa2j4ziO42SZkg1e0iaCC9r/TO27gOCnfjOwEfg+8FEzuzc5/iqCW9rdzey+1Hk3AReb2dLMNQqx\nyQ2anaUdYrJ7x6Slnyiu/t0GP8jE1E6dcun2OvjvAZcQosrtA5wGXCNpnpltJgzZP5nu3BPWAXsU\ncP1x6vWtyx7Stsz0DMoyqOKPDcdxHKc53e4bOu7gzeyiVHK1pOsJw++vB1ZOp8xarUatVmPu3LnM\nmjWLWq22TcB7IDcddoV0WAoxlLJxtD6/W+nh4VGWLl1c2vUb6bS9Z7L8UE+W03V2vVb1ndVURn00\n0qOjoyxe3P37EyLsNd7/UPI/m16GVGtyvM4uu8CmTduW33i9atUq1q5dSz8x3fbeSF988Shnn11+\n+wJYtmzZlPVPJ93O91kv23trvdVr7+2kh4eXMTQ0tc/L6OgoGzduZGxsjNFWtujJAsVP4mxjE/DO\nNvLdAXw4ef0qYAuwWybPamBJzrlWBAsXjhRSThHASI+u0/z4yEhrHQVVf1RaWtGOliJo5/0UVS9J\nO5pS+y5jK6K9x9TWB/GzVAS9qpd2iElLEX1Ds7besQ0+J8/uwBrgXWb2lWSS3XrCJLsLkzx7E57y\nDzezKzPn21Q0TUY7Tl16Ra/sXDHZvWPSEgu9rNsq2eCraAKLqZ0606d0X/SSdgaenyS3A56jMIZ4\nH3A/MAxcDKwF5gKnE+zrKwHM7AFJXwLOkLQ+OeczwI2EyXddIZbO3XGc4ol1vo3jRMVkj/a2dQht\niDDEvgV4MvX6fGAGYZLdOuCPwFiyf69MGU8FPgf8AXgYuDSbJ5W34yELs8EbhmnvOs2P93TILvww\nnXQbaXF8fOsBgzisig/Rl8IgfpaKIKbv45i0dHuIvp118HWaO8Q5vI0yNgPHJ1vl6JUv+pgQ1nzo\nqQ0bijTRq5LjOM6g4L7onbaJye4dk5ZYcBv8RIpo7zHNt+mVlpjaqVMuHfmidxzHiZlYOnfoXfhR\no0Xc5zY3I/rfgE4HDGwHX+9VS2uDWLTEogNcy2TEpKVfiKnOxsbqPbmOaD2DpT7SeqaLemQEi+ke\nVUmLx4MfIMKv+k7L2PrXcZzWpGf0r1gBc+eG1z6j3ykbt8EPEDHZvWPSEgtug5/IoLX3Xq3Jj6md\nOuXS1XjwSZ6lku6W9IikEUkHZI5XOh581ZxwOI7jOK3pdt/QcTx4SScBHwTeDxxM8Fp3paRdUtl6\nHg8+JjvL8HC9bAlAXHXiWvKJSUu/EFOdzZpVL1vCODHVi2vJp9t9Q0fx4CUJWAycbmYrk30LCZ38\n24Fzy4oH7zixUcQciVDO1r9OXNRqZSuIk9FRn49QBh35ope0L/Br4GAzuz6V7zLgD2a2qKx48DGt\njXVf9OVqiQW3wU9k0GzwvSKmdtoOVYwX0A7d9kXf6RD5nOT/usz+9aljPYsHnyaiURjHcRzH6Tnd\nXCY37d8lncaHhhAPHuKIPwyjBEtGufGH07anbseHhiGkUF4j3Sh/K0NNjg8xe3b14kO3G088Gw+8\n8bqK8eD78f4V0b5a5e9le8+/PixfHtIrVgTNY2N1ajVYvLj467WbjunzAsuo1yONBw/sSwg8My+T\n73LgguR1z+LBj4yYLVkSNhgZf93N2AKzZ7fwJEHQ0uz47NnFaIkq2EzL64z05kJtMIgBQqhQsJmY\ngocM4mepCKoYEKhXfUOztt6pDV7A3cBZZnZ6sm8GYfj9BDM7r6x48P20HjUme1o/zRfoN9wGPxG3\nwU+PmL4z2mHRIli+vPvXiYlefR93ug5+Z0m1JAb8eDx4Sc9KWuYy4CRJfyvpRcBywpP+1wDM7AGg\nEQ/+ryS9BPgyXY4H7zhONfD5No6TTzuT7A4Gfp5sM4Dh5PUwgJmdAZwJnAP8lDBxboGZPZwqYzGw\nErgI+CHwIHBEN3+6+3rUicSiI1AvW8A4MdVLTFr6hYadNwZiun8xafH2nk+3tRQRDx4zGybp8Cc5\nXul48I7jOFXDffSXz8AGm9m4cahsCeMM9fDTrKZW16GW58+eXZSS5ixZMtSbC7VBL+9PK2LS8v+3\nd+7BdlX1Hf98h0pR0EkuAiGAgmilIITSF4PQXNSWwFB5FItIawDBChQpHaBAsUlmzEiHjAQcqSUY\nbob6ZLSBiLwsORkhVaM0AREsCBeSAAM0uRDJC3JX/1jr3rvvznnse88+e699zu8zs+49e6919vqe\ntfdvr73X6xcz4yuP/ogqj1IzH0fZ19L4c9Ef0Tz4/rIFjNLpc9S1FXwv0nowRjwD28o0djV/ChrF\nBn/FS7oij6XyiGmBLaM+vXSO2l3oJipqtbHR8/Pm1UY/l93lEk+fT61sAaOUWSbpqSSwvNEUrsKJ\n51qpDkX5YM9CTFpiupZiGhPVS+eoq97gk0/1g4PxPNUbhtE5yl7/3fqaW2PnqBy61h98L86DjyWf\nqlG18rd58OOJqcm1SvedPI8z/phxd4FV6Rx1fB58NhGaG3zFJ8MLddI09BmfN6++2smjGxNFUqbQ\nCfr6vKE0Cl5f89DXl4+WVvlkCUUNhKwKEbVEG2RfHdXoPHn2wT+JdywzEg4ficjoMz5XHnqo1qlD\nT5hY+sJmz66VlnfauJcvL67fe+PGFotFLq+1XFBy48b2dWRZuBJaa9mwoX0t3URMfaox9TXHct+B\nuLT00jnKsw9+h3Pu5fTOLD7jc9RgNOGcc8pWYBj5EGufapF9zXk0ePVia1DZ4wGKJJc+eElzgSuA\nIWAb8FPgGufcs1l8xqeONek+uYULYelS/3nFCpg5038+9VQIzoNyp0p98L1Klc5Rfn2rvdMH399v\nzfT1sPtKucTQB59XBT8L2APfTL8PcC1wCHBY+P8Q8B7n3LrEdxYD051zs1LHymWQXVFGX6XKo1ep\n0jmyCn7iHHkktPKa2YvYfaVcYqjgc2mid87dm9j8paT/Bp4FZuPf5ht+td7OPPzBDw1BEf7gs/hT\nzuJ/OIt/53a3k/09Zfk/TmsoLr/G8bGcH8/E/UOPUKv1jj94b9u+/33NmtXMnevP35Qp3t94Wdd3\nUf7gs237e1N5+Y9tDwzAwEBx9t5sOyZ/8K2ul3r1S8f8wU8kAA/iHdAcRAuf8an9Lg8uvnh5Lsdp\nRR5+mYvyydyLfrOda12+3eg3mx7yBz9jxvK2j5EXMdkYLC9bwigxaSnqHBVVNzSz9Y7Mgw8+4Z8F\nvuqc+2KYMtfQZ3zqu26imsqcd1ml5t+i5n/GRpXOUV50exN9cpDdvHkwZ47/XPYgu5iI6ZqNSUtR\nxNBEn1cf/ALgLmAtsDfwBeBY4HDn3FpJVwLXAOcCT+H76I8FPujGu5XNrQ++KKpUefSikUG1zlFe\ndHsFnySmPvheXHQnC1WznzyIoYLPax78fsC38IPsvgdsAY52zq2FzD7jcyXZN1k28WiplS1glHjK\nxLRUn1rZAka57rpa2RJG6e+vlS0hQa1sAaPEZGOd1pLXILuzMqRp6jM+bwYG4nmSNsrFIWjzXdYl\n/hrlk2yiX7Nm7E217Cb6io1vNLqcrl2LvkrN3lXSWkWqdI7yopea6Mtuiu718QB9fe2v9Dh1avet\n0NhNTfSGYeRALH2mhpGVVktBZwl5LAVt7EwXV/C1sgWMEkufT5lr0aeJpUwgLi3z5tXKllA5yl5b\nvL9/rBVhxoza6Oey395juq5NS30q0QdvVANbiz4+6k3xrDfrM7autJiIaW3xadPKVjBGL45D2rm7\nIGsv1Zh9dVN3gfXBR5BP1fp3q0YvnqNu7oPPsu5FWfe1mKbJ9eI9sBe1dHwefJ5kMfiYBnXEdEEZ\n9cnL61aVnuq7uYLf+RhmP/WwCr43tEQzyE7SRZKelbRF0s8lHTuZ42QZ1NHKx3degzr8FKzmodYi\n3rU7hysjvdT3lKTVtQLNrxXniqvcYzpH1aFWWs6SWobyqJWY93hiuq57SUthFbykM4GFwBeBI4GV\nwD2SDuhEfi0X4c8J0XqI6Oobbmgar4LmVxdVJlmISQvEoyWucimfvr6Wz8/A6pZp+vo6oy+99jfc\nUGdfWRRzLWV5yVl9/PFRvORAcTYWQ7kU+Qb/j3jnMl93zv3aOfd54EXgwk5kNuTdyUVBLFqWLo1D\nB8RTJp54tMRVLuWTpbVuzpyhQqZhZXvYGCrtYWNnirmWsrzkDM2ZU8hLTpZKdeiyywp52IihXAqp\n4CXtChwF3J+Kuh84phN5Dg4OduKwkyIWLStWDJYtYZRYysQzWLaAUeIql2pQVJllediYPXuwkIeN\nLMyYMVhMRrR+8Jk3b7Bp/NSpOenIUKkOzp4dRYsqdP7aLWqa3LuBXfAe5JK8DEx4YkmWpUdXAyxZ\n0uQYY3/bpXU32+pmUnK7uFsTT/NvXE3R8WiJq1zKJw9b98cZ+9sNWrJRUFN06qc0Hncwvlw61X0R\n0/14vJb6wpbsJGasXNrVEuU8+LwGprQ8SqEDYBrntXFjcVLKHfQzHtNSn5i0FEEevzfTEQoq16i0\nRHwtlastjvtxfcYyb1dLURX8q8AOvBe5JPvg++FHqcLUHsMw8sHs3TA6RyF98M657cAvgL9IRf05\nfjS9YRiGYRg5UmQT/ZeB2yX9DF+pfw7f//61AjUYhmEYRk9QWAXvnPuupD2Ba4F9gceAk5xza4vS\nYBiGYRi9QnRL1RqGYRiG0T6Vdxcr6ShJw5IeqhM3XCc8UpKWmZL+S9Irkt6Q9LSk/5D0zhzzH2jw\nm1eG+GFJpzf43rIO6Lg2tb8/7O9L7b8r7P9YXhrqaNpP0i2S1kraJmld2N4vlW4fSTeG87M1pPuh\npBNz0rGXpJvDks1bJb0k6Ufp3x50bJf0nGIeCl0wsdh72bYe8ind3s3WW2op1d4rX8ED5wOrgKMl\nHdIgfloifLRoLZIOBe7FT0ztBw7Dj0EYAn43x/wd8ADjf+804KQM38uzKccBW4ErJL27WUJJ+wIn\nAj/Fl1/uSDoI+DlwKPBp4GDgb/DnYZWk94Z0BwKP4Ad/XgUcjr9e7gb+LSc53wP+CDgP+ABwMnAP\nkF7fbDbwv8AU4ISc8u4GYrH3sm0d4rB3s/XmlGvv6XWTqxSAtwMbgVn4VfGuT8UPA6eXrQX4B2Bt\nARoGgLuaxNctj/C9ZTnquA1vKGuAGxP7+4OGvsS+q4Gf4Fc03JqMy1HPD4G1wG51ztk64AepdO+o\nc4x35aBjSvj9H8mQ9kl8xXALcEenr50qhFjsPQZbD3mVbu9m6021lG7vVX+DPwN4zTl3L75gPi0p\nPXCwqObNelp2CXEvAntJ6i9ARwzNucJf2FcBn5P0vrqJfFPUecAi59xK4Cngb3MV4psITwC+6pzb\nmoxzzm0BbgZODOlmhXSb08dxzr2eg5zfhnCKpIZvc5KOA/YHvgEsAj7e6u2oR4jF3mOxdSjf3s3W\nG1O6vVe9gv8MsDh8XopvLjolleZ2SZsS4awCtZwatu8Avgk8GPpg7pJ0WYdu2rNSv3eTpC91IJ9W\nOOfcPcDDwPwGaWbiFzv6VthehC/HPPkA/ib0RIP4J0L8wYntjuCcews4B99kOCRppaTrJf1JKuln\ngO865zY551YBj+Ob8HqdWOw9FluHOOzdbL0OMdh7ZSt4Se8HPoxvIhopzCXsfNFcDsxIhNwGk2XV\n4pwbds6dh39Kuxx4HrgCeDL02eXJCsb/3hnA9Rm+l/d0ipE3i38CPiHpqDppzge+nXiKvh14fx0D\nKIJC3oScc98HpgN/ie+LOwb4iaSrASS9C/+GeGvia524GVaKWOw9MluHOOzdbL0Bpdt73n0gRQXg\nOnzT0JuJ8FYI+4U0RfXJNdOyf4PvTME727ktRx0DNOlbA14Dzq2zfylwe6d0AN8BfoR/ih/GDzCZ\nAmwJZZQst2Hglhy17IlfJvmaBvH/HDRMDemuLuoaTmhYBGwD3obvh0tfS28GbccUrS2WEIu9x2Lr\n4bil27vZ+qR0FmbvlXyDD/1us/H9Pumn10fxfT2xaDm33vecc0PAS8DuOUtq9mT+a/yIzlFC3+GM\nENcprgGOw4+gHeFs/E3vCMaX2WeBMyW9I4+MnXP/B9wHXCTp7cm4kMfFwD3OuY0h3d9L2umcSJqS\nh54GPIH3trgb/sn9K4wvkyPxg4J68i0+FnuP0NYhPns3W29NcfZexhNMDk9ApwDbgal14q4Engmf\ni3iib6kFfyHfjJ+ScTB+ysa/Bn1n5ahlAD+qdx/GT5vZK8R/Ev8kfRHwe+FC+jp+RPDeOetYltr3\nlZD3DvyT9iPAgjrf3TXo2enNow0978PfYB8GjgcOwI/yfRhYD7w3pDsIeAFvgGcAHwQOAS4EnstB\nx57Ag/gb3hEhv08EbfeFfcPAh+p896+BTcAenbyeYwyx2HtMth7yLN3ezdabaind3jtiCJ0OwJ3A\nvU1O8I5gYEVU8M20HBQ0XBEM4WlgM9673krg7Jy13BbyS4fnE2k+iZ+/+xp+xO+yehdYDjruSu3b\nC3g9nJs/DrqObvD9JcBDOWvaHz/ieR3+Jr0+bE9PpZsG3AT8Bj+VZz1+XvNpOWjYFT8I6WfABuAN\n/NzXBfhmw5uAJxt8d/eQ/vxOXs8xhljsPSZbD3mWbu9m6011lG7vtlStYRiGYXQhleyDNwzDMAyj\nOVbBG4ZhGEYXYhW8YRiGYXQhVsEbhmEYRhdiFbxhGIZhdCFWwRuGYRhGF2IVvGEYhmF0IVbBG4Zh\nGEYXYhV8xEiqSRoOoQyvS0ktcyU9VqaGJJIGEmXzV2XrMYx2MXtvjNn75LAKPm4c3u/0NPx6zh1H\n0oHBiNIuH68H/qwIDSk9/UFPXyrqEmDf8NmWYzS6AbN3s/dc+Z2yBRgt2eyce7mEfMf5THbOvYFf\nG7ks0no2AZukwlw7G0YRmL17zN5zwN7gK4ak6ZK+IelVSW9I+h9J/Yn4v5P0tKRtkp6SdH7q+8OS\nLpB0h6TfSvqNpLMTSZ4J/1eFtA+G741rsgtNZsskXSppnaQNkhbXcdN4ZdCzWdKjybwSbw+nS3og\n/J7HJX1sJB7vjQnglZB2cduFaBgVwezd7L0drIKvEMF38QrgPXjXlYcBcxLxp+FdNX45xN0I3Czp\n5NSh/gX4T7y7wu8AiyUdEOJG+v5OwDcVnt5E0nHAocBHgTOB04BLE3rm431kXwT8PvAl4N8lnZQ6\nznxgYdCzCvh2+K3PAyP9bYcGPZdiGD2A2bvZe9vk7cLQQn4BWA7clNi+AO+Gsa9B+oeBW1P7bgN+\nnNgeBuYntnfBN8V9KmwfGNIclTrOXOCxxPYA8Bx4j4Rh3y3AA+Hz7nh3mR9OHWchcHcqrwsS8dPD\nvmPCdn/YbvSbO+4S2IKFIoLZu9l73sH64KvFHwBrnHMbGsQfAtya2vcw8PHUvkdHPjjndkh6Bdh7\nEnp+5YLVBV4E/jR8PhTYDbhPUjLN24BnG+kJx2CSegyjmzB7N9rCKvjqMZlRJulRp2/WiZ9Md81b\nTY4z8v9kfNNbs/xHt51zLgykse4jwzB7N9rACrVaPAIcIWnPBvFPAMem9h0LPD6BPLaH/7tkSNts\nusqvgG3Agc65Z1JhbYf0GEY3YfZutIW9wVeLbwJXAXdKugp4AfgQ8Lpzroafu3qHpF8ADwCzgE/h\nB8Nk5WVgCzBL0vPAVufcaw3SNny7cM5tkrQAWCD/iP5jYA/gaGCHc25RRj3P4W8sJ0v6AX4aUZnT\ndwyjKMzezd7bwt7gK4RzbjMwE1gHLAMew4+qHQ7xd+IXhLgM/xR/CXChc+7uCeTxFvB54HxgPX70\nLXijSz7Bp7d32uec+wJ+sM7lwC+B+/E3n2dS32mmZz3+N84HXsKPGjaMrsfs3ey9XTR+zIQRE5Jq\n+JGsl5StJVYkDQNnOOe+X7YWw2gHs/fWmL1PDHuDjxsHfFbSJkl/WLaYmJD0NUmbsGUrje7B7L0B\nZu+Tw97gI0bSdPzUE4B1zrntzdL3EpL2At4ZNl8KzZmGUVnM3htj9j45rII3DMMwjC7EmugNwzAM\nowuxCt4wDMMwuhCr4A3DMAyjC7EK3jAMwzC6kP8HYWpILhDBoegAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c7c00f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# box plot of all numeric columns grouped by continent\n",
    "drinks.boxplot(by='continent')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Grouped Histograms: show one histogram for each group"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c7be550>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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T7TDfTHdG5KPmXdu9HMcb6ZLekcCPAR8C/nFlHHQfe/lH4Pn9L8i76VLtofOu\nfR/jObT/hX0K3Rc6vaa/P/F4gN+n+8jPs+k+8rMd+H/0F6zaqOPrl70ROB74IWAz3fHKv1+E8QEX\nArcDJ/GdH6Uar31ht99641uC7ffbdDuEH6L7KPE2upnS5yz6tltvfIu+7fYx3hFwwdjjmW2/uQ/2\nPv4DvYzuXcke4JN0x0/mXte9HMO7gJ10YeYWui87esKqPq8Fvkh30st2um9EnHvt+xjPZmBvf7tn\n7P7bJh0P8F3AW+imve4EPgA8Yt5jW298dGckf5TupJuvAzf37Y9YtY4NOb41xrRy+4178/u4qONb\ngu33h33Ne/oxXEa/81/0bbfe+BZ92+1jvN/6GOCst5+XApYkqUELdQ6AJEmaDgOAJEkNMgBIktQg\nA4AkSQ0yAEiS1CADgCRJDTIASJLUIAOAJEkNMgBIktSg/w8irvOIWk5JHQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f6caef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# reminder: histogram of beer servings\n",
    "drinks.beer.plot(kind='hist')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x000000001F838390>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000000001FA829B0>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x000000001FC287F0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000000001CB23D68>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x000000001FC5C518>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000000001FDE2278>]], dtype=object)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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BJrbExof0u289I5/clPYc1wdmoxtrnZD38vt9pB/qHo0nJE0g9X6/rt0Lyp6b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\nD+qO4ziO0xL+D9eim5FP69x6AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f85db70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# histogram of beer servings grouped by continent\n",
    "drinks.hist(column='beer', by='continent')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x000000001F865F60>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x00000000204A8320>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x00000000202DC0B8>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x00000000202F6860>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x000000002070E828>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000000020721518>]], dtype=object)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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UNszyCEilbishSQcCm7Dut/IdgPW+lUuKPDq3bbbZgSxf/r/k21Euj3PJlbp1TBIRUcnb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\nky2xAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x20477a58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# share the x axes\n",
    "drinks.hist(column='beer', by='continent', sharex=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x0000000020DF74A8>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000000020CE1128>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x0000000020D03D68>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000000021392588>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x00000000213BB080>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x00000000214E2550>]], dtype=object)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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mn3vuuWUUmzNacv4uY3qWMf0sXfoz4NAu97q9i21Hu8y7F1WUUU051QRBKr+M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\nasKdupmZWU38f1Z7hBs9+w8VAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x21008780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# share the x and y axes\n",
    "drinks.hist(column='beer', by='continent', sharex=True, sharey=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x000000002103D358>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000000021A4B470>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000000021A71208>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x00000000221EAB00>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000000022210AC8>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x00000000223237B8>]], dtype=object)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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juqG3vCwjNfat1LbXGvY/kzratStn1peIuCYi3gJsDOxC6qAXwNck7QUgaSPS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\nwtzQm5mZVZgbejMzswpzQ29mZlZh/w+p6bGGUceWBgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x210082e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# change the layout\n",
    "drinks.hist(column='beer', by='continent', sharex=True, layout=(2, 3))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Assorted Functionality"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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kSUvZJK4AHAocWVXfAtIzf1tgS+D42RlVdSNwMrDLWCOUJGmJu/04N5ZkH2AZt57RV8/i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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x22aab470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# saving a plot to a file\n",
    "drinks.beer.plot(kind='hist', bins=20, title='Histogram of Beer Servings')\n",
    "plt.xlabel('Beer Servings')\n",
    "plt.ylabel('Frequency')\n",
    "plt.savefig('beer_histogram.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[u'dark_background', u'bmh', u'grayscale', u'ggplot', u'fivethirtyeight']"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# list available plot styles\n",
    "plt.style.available"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# change to a different style\n",
    "plt.style.use('ggplot')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
